There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” 

But these aren’t checkpoints on a single journey to automation.

DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.

And now, a new frontier is emerging inside IT operations itself: Agentic AIOps.

It’s not another dashboard or a new methodology. It’s a shift from detection to autonomous resolution—freeing teams to move faster, spend less time firefighting, and focus on what actually moves the business forward.

In this article, we’ll break down:

• What DevOps, MLOps, AIOps, and agentic AIOps actually mean 
• How they fit into modern IT (and where they don’t overlap)
• Why agentic AIOps marks a transformational leap for IT operations

Let’s start by understanding what each “Ops” term means on its own.

Why “Ops” Matters in IT Today

Modern IT environments are moving targets. More apps. More data. More users. More cloud. And behind it all is a patchwork of specialized teams working to keep everything running smoothly.

Each “Ops” area—DevOps, MLOps, AIOps, and now agentic AIOps—emerged to solve a specific bottleneck in how systems are built, deployed, managed, and scaled and how different technology professionals interact with them.

Notably, they aren’t layers in a single stack. They aren’t milestones on a maturity curve. They are different approaches, designed for different challenges, with different users in mind.

• DevOps bridges development and operations to accelerate application delivery.
• MLOps operationalizes the machine learning lifecycle at scale.
• AIOps brings intelligence into IT incident management and monitoring.
• Agentic AIOps pushes operations further—moving from insights to autonomous action.

Understanding what each “Ops” area does—and where they intersect—is essential for anyone running modern IT. Because if you’re managing systems today, odds are you’re already relying on several of them.

And if you’re planning for tomorrow, it’s not about stacking one on top of the other. It’s about weaving them together intelligently, so teams can move faster, solve problems earlier, and spend less time stuck in reactive mode.

DevOps, MLOps, AIOps, and Agentic AIOps: Distinct Terms, Different Challenges

Each “Ops” area emerged independently, to solve different challenges at different layers of the modern IT stack. They’re parallel movements in technology—sometimes overlapping, sometimes interacting, but ultimately distinct in purpose, users, and outcomes.

Here’s how they compare at a high level:

What is DevOps?

DevOps is a cultural and technical movement that brings together software development and operations to streamline the process of building, testing, and deploying code. It’s responsible for replacing much of the slow, manual processes involved in automating pipelines for building, testing, and deploying code. Tools like CI/CD, Infrastructure as Code (IaC), and container orchestration became the new standard.

Bringing these functions together led to faster releases, fewer errors, and more reliable deployments.

DevOps is not responsible for running machine learning (ML) workflows or managing IT incidents. Its focus is strictly on delivering application code and infrastructure changes with speed and reliability.

• Used by: Software developers, DevOps engineers
• Purpose: Automate and accelerate the software delivery pipeline
• Key Tools: Jenkins, GitLab CI/CD, Terraform, Kubernetes

Why DevOps Matters:

DevOps automates the build-and-release cycle. It reduces errors, accelerates deployments, and helps teams ship with greater confidence and consistency.

How DevOps Interacts with Other Ops:

• MLOps adapts DevOps principles—like CI/CD and pipeline automation—to machine learning workflows.
• AIOps consumes the telemetry—metrics, events, logs, and traces—that DevOps pipelines generate to power incident detection and analysis.

What is MLOps?

As machine learning moved from research labs into enterprise production, teams needed a better way to manage it at scale. That became MLOps.

MLOps applies DevOps-style automation to machine learning workflows. It standardizes how models are trained, validated, deployed, monitored, and retrained. What used to be a one-off, ad hoc process is now governed, repeatable, and production-ready.

MLOps operates in a specialized world. It’s focused on managing the lifecycle of ML models—not the applications they power, not the infrastructure they run on, and not broader IT operations.

MLOps helps data scientists and ML engineers move faster, but it doesn’t replace or directly extend DevOps or AIOps practices.

• Used by: ML engineers, data scientists
• Purpose: Automate and govern the ML model lifecycle
• Key Tools: MLflow, Kubeflow, TFX, SageMaker

Why MLOps Matters:

MLOps ensures machine learning models stay accurate, stable, and useful over time.

How MLOps Interacts with Other Ops:

• Adapts DevOps principles, borrowing ideas like pipeline automation and versioning for model management.
• Supports AIOps use cases by providing trained models that can detect patterns, anomalies, and trends across IT environments. MLOps and AIOps can work together, but they solve very different problems for different practitioners.
• MLOps is not an extension of DevOps, nor is it a prerequisite for AIOps. It addresses a unique set of needs and typically operates in its own pipeline and toolchain.

What is AIOps?

AIOps brought artificial intelligence directly into IT operations. It refers to software platforms that apply machine learning and analytics to IT operations data to detect anomalies, reduce alert noise, and accelerate root cause analysis. It helps IT teams manage the growing complexity of modern hybrid and cloud-native environments.

It marked a shift from monitoring everything to understanding what matters.

But even the most advanced AIOps platforms often stop short of action. They surface the problem, but someone still needs to decide what to do next. AIOps reduces the workload, but it doesn’t eliminate it.

• Used by: IT operations, SREs, NOC teams
• Purpose: Improve system reliability and reduce mean time to resolution (MTTR)
• Key Capabilities: Correlation engines, anomaly detection, predictive analytics

Why AIOps Matters:

AIOps gives IT operations teams a critical edge in managing complexity at scale.

By applying machine learning and advanced analytics to vast streams of telemetry data, it cuts through alert noise, accelerates root cause analysis, and helps teams prioritize what matters most.

How AIOps Interacts with Other Ops:

• Ingests telemetry from across the IT environment, including metrics, events, logs, and traces from systems managed by DevOps, but operates independently of DevOps workflows.
• May use machine learning models—whether built-in, third-party, or homegrown—to improve anomaly detection and predictions, but does not rely on an internal MLOps process or teams.

What is Agentic AIOps?

Agentic AIOps is the next evolution inside IT operations: moving from insight to action.

These aren’t rule-based scripts or rigid automations. Agentic AIOps uses AI agents that are context-aware, goal-driven, and capable of handling common issues on their own. Think scaling up resources during a traffic spike. Isolating a faulty microservice. Rebalancing workloads to optimize cost.

Agentic AIOps isn’t about replacing IT teams. It’s about removing the repetitive, low-value tasks that drain their time, so they can focus on the work that actually moves the business forward. With Agentic AIOps, teams spend less time reacting and more time architecting, scaling, and innovating. It’s not human vs. machine. It’s humans doing less toil—and more of what they’re uniquely great at.

• Used by: IT operations, SREs, NOC teams
• Purpose: Close the loop between detection and resolution; enable self-managing systems
• Key Capabilities: Intelligent automation, safe autonomy, policy-driven guardrails

Why Agentic AIOps Matters:

Agentic AIOps closes the loop between detection and resolution. It can scale resources during a traffic spike, isolate a failing service, or rebalance workloads to cut cloud costs, all without waiting on human input.

How Agentic AIOps Interacts with Other Ops:

• Extends AIOps capabilities, taking incident insights and acting on them autonomously.
• Operates on telemetry from across the IT environment, including systems built and managed with DevOps practices.
• May incorporate ML models to inform decision-making, whether those models are homegrown, third-party, or built into the platform.

Agentic AIOps is not a convergence of DevOps, MLOps, and AIOps. It is a visionary extension of the AIOps category—focused specifically on automating operational outcomes, not software delivery or ML workflows.

These “Ops” Areas Solve Different Problems—Here’s How They Overlap

Modern IT teams don’t rely on just one “Ops” methodology—and they don’t move through them in a straight line. Each Ops solves a different part of the technology puzzle, for a different set of users, at a different layer of the stack.

• DevOps accelerates application delivery.
• MLOps manages the machine learning model lifecycle.
• AIOps brings intelligence into IT monitoring and incident management.
• Agentic AIOps pushes IT operations toward autonomous resolution.

They can overlap. They can support each other. But critically, they remain distinct—operating in parallel, not as steps on a single roadmap.

Here’s how they sometimes interact in a real-world environment:

DevOps and MLOps: Shared ideas, different domains

DevOps builds the foundation for fast, reliable application delivery. MLOps adapts some of those automation principles—like CI/CD pipelines and version control—to streamline the machine learning model lifecycle.

They share concepts, but serve different teams: DevOps for software engineers; MLOps for data scientists and ML engineers.

Example:
A fintech company uses DevOps pipelines to deploy new app features daily, while separately running MLOps pipelines to retrain and redeploy their fraud detection models on a weekly cadence.

AIOps: Using telemetry from DevOps-managed environments (and beyond)

AIOps ingests operational telemetry from across the IT environment, including systems managed via DevOps practices. It uses pattern recognition and machine learning (often built-in) to detect anomalies, predict issues, and surface root causes.

AIOps platforms typically include their own analytics engines; they don’t require enterprises to run MLOps internally.

Example:
A SaaS provider uses AIOps to monitor cloud infrastructure. It automatically detects service degradations across multiple apps and flags issues for the IT operations team, without depending on MLOps workflows.

Agentic AIOps: Acting on insights

Traditional AIOps highlights issues. Agentic AIOps goes further—deploying AI agents to make real-time decisions and take corrective action automatically. It builds directly on operational insights, not DevOps or MLOps pipelines. Agentic AIOps is about enabling true autonomous response inside IT operations.

Example:
A cloud platform experiences a sudden traffic spike. Instead of raising an alert for human review, an AI agent automatically scales up infrastructure, rebalances workloads, and optimizes resource usage—before users notice an issue.

Bottom Line: Understanding the “Ops” Landscape

DevOps, MLOps, AIOps, and Agentic AIOps aren’t milestones along a single maturity curve. They’re distinct problem spaces, developed for distinct challenges, by distinct teams.

In modern IT, success isn’t about graduating from one to the next; it’s about weaving the right approaches together intelligently.

Agentic AIOps is the next frontier specifically within IT operations: closing the loop from detection to real-time resolution with autonomous AI agents, freeing human teams to focus where they drive the most value.

Your tech stack is growing, and with it, the endless stream of log data from every device, application, and system you manage. It’s a flood—one growing 50 times faster than traditional business data—and hidden within it are the patterns and anomalies that hold the key to the performance of your applications and infrastructure.

But here’s the challenge you know well: with every log, the noise grows louder, and manually sifting through it is no longer sustainable. Miss a critical anomaly, and you’re facing costly downtime or cascading failures.

That’s why log analysis has evolved. AI-powered log intelligence isn’t just a way to keep up—it’s a way to get ahead. By detecting issues early, cutting through the clutter, and surfacing actionable insights, it’s transforming how fast-moving teams operate.

The stakes are high. The question is simple: are you ready to leave outdated log management behind and embrace the future of observability?

Why traditional log analysis falls short

Traditional log analysis methods struggle to keep pace with the complexities of modern IT environments. As organizations scale, outdated approaches relying on manual processes and static rules create major challenges:

• Overwhelming log volumes: Exponential growth in log data makes manual analysis slow and inefficient, delaying issue detection and resolution.
• Inflexible static rules: Predefined rules cannot adapt to dynamic workloads or detect previously unknown anomalies, leading to blind spots.
• Resource-intensive and prone to errors: Manual query matching requires significant time and effort, increasing the likelihood of human error.

These limitations become even more pronounced in multicloud environments, where resources are ephemeral, workloads shift constantly, and IT landscapes evolve rapidly. Traditional tools lack the intelligence to adapt, making it difficult to surface meaningful insights in real time.

How AI transforms log analysis

AI-powered log analysis addresses these shortcomings by leveraging machine learning and automation to process vast amounts of data, detect anomalies proactively, and generate actionable insights. Unlike traditional methods, AI adapts dynamically, ensuring organizations can stay ahead of performance issues, security threats, and operational disruptions.

The challenge of log volume and variety

If you’ve ever tried to make sense of the endless stream of log data pouring in from hundreds of thousands of metrics and data sources, you know how overwhelming it can be. Correlating events and finding anomalies across such a diverse and massive dataset isn’t just challenging—it’s nearly impossible with traditional methods.

As your logs grow exponentially, manual analysis can’t keep up. AI log analysis offers a solution, enabling you to make sense of vast datasets, identify anomalies as they happen, and reveal critical insights buried within the noise of complex log data.

So, what is AI log analysis?

AI log analysis builds on log analysis by using artificial intelligence and automation to simplify and interpret the increasing complexity of log data.

Unlike traditional tools that rely on manual processes or static rules, AI log analysis uses machine learning (ML) algorithms to dynamically learn what constitutes “normal” behavior across systems, proactively surfacing anomalies, pinpointing root causes in real time, and even preventing issues by detecting early warning signs before they escalate.

In today’s dynamic, multicloud environments—where resources are often ephemeral, workloads shift constantly, and SaaS sprawl creates an explosion of log data—AI-powered log analysis has become essential. An AI tool can sift through vast amounts of data, uncover hidden patterns, and find anomalies far faster and more accurately than human teams. And so, AI log analysis not only saves valuable time and resources but also ensures seamless monitoring, enhanced security, and optimized performance.

With AI log analysis, organizations can move from a reactive to a proactive approach, mitigating risks, improving operational efficiency, and staying ahead in an increasingly complex IT landscape.

How does it work? Applying machine learning to log data

The goal of any AI log analysis tool is to upend how organizations manage the overwhelming volume, variety, and velocity of log data, especially in dynamic, multicloud environments. 

With AI, log analysis tools can proactively identify trends, detect anomalies, and deliver actionable insights with minimal human intervention. Here’s how machine learning is applied to log analysis tools:

Step 1 – Data collection and learning

AI log analysis begins by collecting vast amounts of log data from across your infrastructure, including applications, network devices, and cloud environments. Unlike manual methods that can only handle limited data sets, machine learning thrives on data volume. The more logs the system ingests, the better it becomes at identifying patterns and predicting potential issues.

To ensure effective training, models rely on real-time log streams to continuously learn and adapt to evolving system behaviors. For large-scale data ingestion, a data lake platform can be particularly useful, enabling schema-on-read analysis and efficient processing for AI models.

Step 2 – Define normal ranges and patterns

With enough log data necessary to see trends over time, the next step in applying machine learning is detecting what would fall in a “normal” range from log data. This means identifying baseline trends across metrics, such as usage patterns, error rates, and response times. The system can then detect deviations from these baselines without requiring manual rule-setting.  It’s also important to understand that deviations or anomalies may also be expected or good in nature and not always considered problematic. The key is to establish a baseline and then interpret that baseline.   

In multicloud environments, where workloads and architectures are constantly shifting, this step ensures that AI log analysis tools remain adaptive, even when the infrastructure becomes more complex.

Step 3 – Deploy algorithms for proactive alerts

With established baselines, machine learning algorithms can monitor logs in real time, detecting anomalies that could indicate potential configuration issues, system failures, or performance degradation. These anomalies are flagged when logs deviate from expected behavior, such as:

• Unusual spikes in network latency that may signal resource constraints.
• New log patterns appearing for the first time, which may indicate an emerging issue.
• Levels of error conditions in application logs increasing could indicate an outage on the horizon or that performance issues are happening. 
• A sudden increase in failed login attempts suggesting a security breach.

Rather than simply reacting to problems after they occur, machine learning enables predictive log analysis, identifying early warning signs and reducing Mean Time to Resolution (MTTR). This proactive approach supports real-time monitoring, less outages by having healthier logs with less errors, capacity planning, and operational efficiency, ensuring that infrastructure remains resilient and optimized.

By continuously refining its understanding of system behaviors, machine learning-based log analysis eliminates the need for static thresholds and manual rule-setting, allowing organizations to efficiently manage log data at scale while uncovering hidden risks and opportunities.

Step 4 – Maintaining Accuracy with Regular Anomaly Profile Resets

Regularly resetting the log anomaly profile is essential for ensuring accurate anomaly detection and maintaining a relevant baseline as system behaviors evolve. If the anomaly profile is not reset there is potential that once seen as negative behavior may never be flagged again for the entire history of that log stream.  Resetting machine learning or anomaly algorithms can allow organizations to test new log types or resources, validate alerts with anomalies or “never before seen” conditions, and reset specific resources or groups after a major outage to clear outdated anomalies. 

Additional use cases include transitioning from a trial environment to production, scheduled resets to maintain accuracy on a monthly, quarterly, or annual basis, and responding to infrastructure changes, new application deployments, or security audits that require a fresh anomaly baseline.

To maximize effectiveness, best practices recommend performing resets at least annually to ensure anomaly detection remains aligned with current system behaviors. Additionally, temporarily disabling alert conditions that rely on “never before seen” triggers during a reset prevents unnecessary alert floods while the system recalibrates. A structured approach to resetting anomaly profiles ensures log analysis remains relevant, minimizes alert fatigue, and enhances proactive anomaly detection in dynamic IT environments. 

Benefits of AI for log analysis

Raw log data is meaningless noise until transformed into actionable insights. Modern AI-powered log analysis delivers crucial advantages that fundamentally change how we handle system data:

Immediate impact

• Sort through data faster. AI automatically clusters and categorizes incoming logs, making critical information instantly accessible without manual parsing.
• Detect issues automatically. Unlike static thresholds that can’t keep up with changing environments, AI learns and adjusts in real time. It recognizes shifting network behaviors, so anomalies are detected as they emerge—even when usage patterns evolve.
• Only be alerted to important information. Alerts from logs, like many alerts in IT, are prone to “boy who cried wolf syndrome.” When a log analysis tool creates too many alerts, no single alert stands out as the cause of an issue, if there even is an issue at all. With AI, you can move towards only being alerted when something worth your attention is happening, clearing the clutter and skipping the noise. 
• Detect anomalies before they create issues. In most catastrophic events, there’s typically a chain reaction that occurs because an initial anomaly wasn’t addressed. AI allows you to remove the cause, not the symptom. 

Strategic benefits

• Know the root cause: AI doesn’t just flag an issue—it understands the context, helping you pinpoint the root cause before small issues escalate into major disruptions.
• Enhance security: Sensitive data is safeguarded with AI-enabled privacy features like anonymization, masking, and encryption. This not only protects your network but also ensures compliance with security standards.
• Allocate resources faster and more efficiently: By automating the heavy lifting of log analysis, AI frees up your team to focus on higher-priority tasks, saving both time and resources.

Measurable results

• Reduce system downtime. Quick identification of error sources leads to faster resolution and improved system reliability.
• Reduce noisy alerts. Regular anomaly reviews result in cleaner logs and more precise monitoring.
• Prevent issues proactively. Early detection of unusual patterns helps prevent minor issues from escalating into major incidents.

Why spend hours drowning in raw data when AI log analysis can do the hard work for you? It’s smarter, faster, and designed to keep up with the ever-changing complexity of modern IT environments. Stop reacting to problems—start preventing them.

How LM Logs uses AI for anomaly detection

When it comes to AI log analysis, one of the most powerful applications is anomaly detection. Real-time detection of unusual events is critical for identifying and addressing potential issues before they escalate. LM Logs, a cutting-edge AI-powered log management platform, stands out in this space by offering advanced anomaly detection features that simplify the process and enhance accuracy.

Let’s explore how LM Logs leverages machine learning to uncover critical insights and streamline log analysis.

To start — not every anomaly signals trouble—some simply reflect new or unexpected behavior. However, these deviations from the norm often hold the key to uncovering potential problems or security risks, making it critical to flag and investigate them. LM Logs uses machine learning to make anomaly detection more effective and accessible. Here’s how it works:

• Noise reduction: By filtering out irrelevant log entries, LM Logs minimizes noise, enabling analysts to focus on the events that truly matter.
• Unsupervised learning: Unlike static rule-based systems, LM Logs employs unsupervised learning techniques to uncover patterns and detect anomalies without requiring predefined rules or labeled data. This allows it to adapt dynamically to your environment and identify previously unseen issues.
• Highlighting unusual events: LM Logs pinpoints deviations from normal behavior, helping analysts quickly identify and investigate potential problems or security breaches.
• Contextual analysis: LM Logs combines infrastructure metric alerts and anomalies into a single view. This integrated approach streamlines troubleshooting, allowing operators to focus on abnormalities with just one click.
• Flexible data ingestion: Whether structured or unstructured, LM Logs can ingest logs in nearly any format and apply its anomaly detection analysis, ensuring no data is left out of the process.

By leveraging AI-driven anomaly detection, LM Logs transforms how teams approach log analysis. It not only simplifies the process but also ensures faster, more precise identification of issues, empowering organizations to stay ahead in an ever-evolving IT landscape.

Case Study: How AI log analysis solved the 2024 CrowdStrike incident

In 2024, a faulty update to CrowdStrike’s Falcon security software caused a global outage, crashing millions of Windows machines. Organizations leveraging AI-powered log analysis through LM Logs were able to pinpoint the root cause and respond faster than traditional methods allowed, avoiding the chaos of prolonged outages.

Rapid identification

When the incident began, LM Logs anomaly detection flagged unusual spikes in log activity. The first anomaly—a surge of new, unexpected behavior—was linked directly to the push of the Falcon update. The second, far larger spike occurred as system crashes, reboots, and error logs flooded in, triggering monitoring alerts. By correlating these anomalies in real time, LM Logs immediately highlighted the faulty update as the source of the issue, bypassing lengthy war room discussions and saved IT teams critical time.

Targeted remediation

AI log analysis revealed that the update impacted all Windows servers where it was applied. By drilling into the affected timeslice and filtering logs for “CrowdStrike,” administrators could quickly identify the common denominator in the anomalies. IT teams immediately knew which servers were affected, allowing them to:

• Isolate problematic systems.
• Initiate targeted remediation strategies.
• Avoid finger-pointing between teams and vendors by quickly escalating the issue to CrowdStrike.

This streamlined approach ensured organizations could contain the fallout and focus on mitigating damage while awaiting a fix from CrowdStrike.

Learning in progress

One of the most remarkable aspects of this case was the machine learning in action. For instance:

• LM Logs flagged the first occurrence of the system reboot error—”the system has rebooted without cleanly shutting down first”—as an anomaly.
• Once this behavior became repetitive, the system recognized it as learned behavior and stopped flagging it as an anomaly, allowing teams to focus on new, critical issues instead.

This adaptive capability highlights how AI log analysis evolves alongside incidents, prioritizing the most pressing data in real-time.

Results

Using LM Logs, IT teams quickly:

• Pinpointed the root cause of the outage.
• Determined the scope of the impact across servers.
• Avoided wasting valuable time and resources on misdirected troubleshooting.

In short, AI log analysis put anomaly detection at the forefront, turning what could have been days of confusion into rapid, actionable insights.

AI log analysis is critical for modern IT

In today’s multicloud environments, traditional log analysis simply can’t keep up with the volume and complexity of data. AI solutions have become essential, not optional. They deliver real-time insights, detect anomalies before they become crises, and enable teams to prevent issues rather than just react to them.

The CrowdStrike incident of 2024 demonstrated clearly how AI log analysis can transform crisis response—turning what could have been days of debugging into hours of targeted resolution. As technology stacks grow more complex, AI will continue to evolve, making log analysis more intelligent, automated, and predictive.

Organizations that embrace AI log analysis today aren’t just solving current challenges—they’re preparing for tomorrow’s technological demands. The question isn’t whether to adopt AI for log analysis, but how quickly you can integrate it into your operations.

Every minute of system downtime costs enterprises a minimum of $5,000. With IT infrastructure growing more complex by the day, companies are put at risk of even greater losses. 

Adding insult to injury, traditional operations tools are woefully out of date. They can’t predict failures fast enough. They can’t scale with growing infrastructure. And they certainly can’t prevent that inevitable 3 AM crisis—the one where 47 engineers and product managers flood the war room, scrambling through calls and documentation to resolve a critical production issue.

Agentic AIOps flips the script. Unlike passive monitoring tools, it actively hunts down potential failures before they impact your business. It learns. It adapts. And most importantly, it acts—without waiting for human intervention.

This blog will show you how agentic AIOps transforms IT from reactive to predictive, why delaying implementation could cost millions, and how platforms like LogicMonitor Envision—the core observability platform—and Edwin AI can facilitate this transformation.

You’ll learn:

• What agentic AIOps is
• The core components driving agentic AIOps
• How agentic AIOps works
• A step-by-step guide to implementing agentic AIOps
• How agentic AIOps compares to traditional AIOps and related concepts
• Real-world use cases where agentic AIOps delivers measurable value
• The key benefits of agentic AIOps
• How LogicMonitor enables agentic AIOps success

What is agentic AIOps?

Agentic AIOps redefines IT operations by combining generative AI and agentic AI with cross-domain observability to autonomously detect, diagnose, and resolve infrastructure issues.

For IT teams floundering in alerts, juggling tools, and scrambling during incidents, this shift is transformative. Unlike traditional tools that merely detect issues, agentic AIOps understands them. It doesn’t just send alerts—it actively hunts down root causes across your entire IT ecosystem, learning and adapting to your environment in real time.

Agentic AIOps is more than a monitoring tool; it’s a paradigm shift. It unifies observability, resolves routine issues automatically, and surfaces strategic insights your team would otherwise miss. This is achieved through:

1. Operating autonomously, learning and adapting in real time.
2. Unifying observability across the entire infrastructure, minimizing blind spots.
3. Automatically resolving routine issues, while surfacing critical insights.

With its zero-maintenance architecture, there’s no need for constant rule updates or alert tuning. The generative interface simplifies troubleshooting by transforming complex issues into actionable steps and clear summaries.

Agentic AIOps isn’t just a tool—it’s essential for the future of IT operations.

Why is agentic AIOps important?

As IT systems grow more complex—spanning hybrid environments, cloud, on-premises, and third-party services—the challenges of managing them multiply. Data gets scattered across platforms, causing fragmentation and alert overload.

Traditional AIOps can’t keep up. Static rules and predefined thresholds fail to handle the dynamic nature of modern IT. These systems:

• Require constant manual tuning
• Struggle to connect disparate data
• Are reactive, not proactive

As a result, IT teams waste time piecing together data, hunting down issues, and scrambling to prevent cascading failures. Every minute spent is costly.

Agentic AIOps changes that. By shifting to a proactive approach, it automatically detects and resolves issues before they escalate. This not only reduces downtime but also cuts operational costs.

With agentic AIOps, IT teams are freed from routine firefighting and can focus on driving innovation. By unifying observability and automating resolutions, it removes the noise, enhances efficiency, and supports smarter decision-making.

Key components of agentic AIOps

Enterprise IT operations are trapped in a costly paradox: despite pouring resources into monitoring tools, outages continue to drain millions, and digital transformation often falls short. The key to breaking this cycle lies in two game-changing components that power agentic AIOps:

Generative AI and agentic AI power autonomous decision-making

Agentic AIOps is powered by the complementary strengths of generative AI and agentic AI. 

While generative AI creates insights, content, and recommendations, agentic AI takes the critical step of making autonomous decisions and executing actions in real-time. Together, they enable a level of proactive IT management previously beyond reach.

Here’s how the two technologies work in tandem:

• Generative AI: This component generates meaningful content from raw data, such as plain-language summaries, root cause analyses, and step-by-step guides for remediation. It transforms complex technical data into easily digestible insights and recommendations. In short, generative AI clarifies the situation, offering valuable context and potential solutions.
• Agentic AI: Once insights are generated by the system, agentic AI takes over. It doesn’t simply offer suggestions; it actively makes decisions and implements them based on real-time data. This allows the system to autonomously resolve issues, such as rolling back configurations, scaling resources, or initiating failovers without human intervention.

By combining the strengths of both, agentic AIOps transcends traditional IT monitoring. It enables the system to shift from a reactive stance—where IT teams only respond to problems—to a proactive approach where it can predict and prevent issues before they affect operations.

Why this matters

Instead of simply alerting IT teams when something goes wrong, generative AI sifts through data to uncover the underlying cause, offering clear, actionable insights. For example, if an application begins to slow down, generative AI might pinpoint the bottleneck, suggest the next steps, and even generate a root cause analysis. 

But it’s agentic AI that takes the reins from there, autonomously deciding how to respond—whether by rolling back a recent update, reallocating resources, or triggering a failover to ensure continuity. 

This ability to not only detect but also act, reduces downtime, cuts operational costs, and enhances system reliability. IT teams are freed from the constant cycle of fire-fighting, instead managing and preventing issues before they impact business operations.

Cross-domain observability provides complete operational visibility

Fragmented visibility creates significant business risks, but cross-domain observability mitigates these by integrating data across all IT environments—cloud, on-prem, and containerized—while breaking down silos and providing real-time, actionable insights. This capability is essential for agentic AIOps, transforming IT from a reactive cost center to a proactive business driver.

Here’s how it works:

• Data integration: Cross-domain observability connects structured data (like metrics and logs) with unstructured data (such as team conversations and incident reports) into a unified stream, ensuring no critical data is missed. This complete integration empowers agentic AIOps to detect and resolve issues across your entire IT ecosystem without human intervention.
• Dynamic response: Unlike traditional systems that wait for manual adjustments, agentic AIOps continually adapts to evolving conditions in real-time. Through intelligent event correlation and predictive modeling, it autonomously adjusts operations to mitigate risks as they arise. 

With agentic AIOps, you gain what traditional IT operations can’t offer: autonomous, intelligent operations that scale with your business, delivering both speed and efficiency. 

Why this matters

Cross-domain observability is essential to unlocking the full potential of agentic AIOps. It goes beyond data collection by providing real-time insights into the entire IT landscape, integrating both structured and unstructured data into a unified platform. This gives agentic AIOps the context it needs to make swift, autonomous decisions and resolve issues without manual oversight. 

By minimizing blind spots, offering real-time system mapping, and providing critical context for decision-making, it enables agentic AIOps to act proactively, preventing disruptions before they escalate. This shift from reactive to intelligent, autonomous management creates a resilient and scalable IT environment, driving both speed and efficiency.

How does agentic AIOps work?

Agentic AIOps simplifies complex IT environments by processing data across the entire infrastructure. It uses AI to detect, diagnose, and predict issues, enabling faster, smarter decisions and proactive management to optimize performance and reduce downtime. 

Comprehensive data integration

Modern IT infrastructures generate an overwhelming amount of data, from application logs to network metrics and security alerts. Agentic AIOps captures and integrates both structured (metrics, logs, traces) and unstructured data (like incident reports and team communications) across all operational domains. This unified, cross-domain visibility ensures no area is overlooked, eliminating blind spots and offering a comprehensive, real-time view of your entire infrastructure.

Real-time intelligent analysis

While traditional systems bombard IT teams with alerts, agentic AIOps uses generative and agentic AI to go beyond simply detecting patterns. It processes millions of data points per second, predicting disruptions before they occur. With continuous, autonomous learning, it adapts to changes without needing manual rule adjustments, offering smarter insights and more precise solutions.

Actionable intelligence generation

Unlike standard monitoring tools, agentic AIOps doesn’t just flag problems—it generates actionable, AI-powered recommendations. Using large language models (LLMs), it provides clear, contextual resolutions in plain language, easily digestible by both technical and non-technical users. Retrieval-augmented generation (RAG) ensures these insights are drawn from the most up-to-date and relevant data.

Autonomous resolution

This is where agentic AIOps stands apart: when it detects an issue, it takes action. Whether it’s scaling resources, rerouting traffic, or rolling back configurations, the system acts autonomously to prevent business disruption. This eliminates the need for manual intervention, allowing IT teams to focus on higher-level strategy.

Now—imagine during a product launch, the agentic AIOps system detects a 2% degradation in database performance. It could immediately correlate the issue with a recent change, analyze the potential impact—$27,000 per minute—and autonomously roll back the change. The system would then document the incident for future prevention. In just seconds, the problem would be resolved with minimal business impact.

Agentic AIOps stands out by shifting IT operations from constant firefighting to proactive, intelligent management. By improving efficiency, reducing downtime, and bridging the IT skills gap, it ensures your IT infrastructure stays ahead of disruptions and scales seamlessly with your evolving business needs.

Implementing agentic AIOps

Implementing agentic AIOps requires a strategic approach to ensure that your IT operations become more efficient, autonomous, and proactive. 

Here’s a step-by-step framework for getting started:

1. Assess your current IT infrastructure: Begin by understanding the complexity and gaps in your existing systems. Identify the areas where you’re struggling with scalability, visibility, or reliability. This will help you pinpoint where agentic AIOps can drive the most impact.
2. Identify pain points: Take a deep dive into the challenges your IT team faces daily. Whether it’s alert fatigue, delayed incident resolution, or inadequate cross-domain visibility, recognize where agentic AIOps can make the biggest difference. The goal is to streamline processes and reduce friction in areas that are stalling progress.
3. Choose the right tools and platforms: Select a platform that integrates observability and AIOps. For example, LogicMonitor Envision offers an all-in-one solution to bring together cross-domain observability with intelligent operations. Additionally, consider tools like Edwin AI for AI-powered incident management to automate and prioritize issues based on business impact.
4. Plan a phased implementation strategy: Start with a pilot project to test the solution in a controlled environment. Use this phase to refine processes, iron out any issues, and collect feedback. Then, roll out the solution in stages across different parts of the organization. This phased approach reduces risk and ensures smooth adoption.
5. Monitor and refine processes: Once your solution is live, continuously monitor its impact on IT efficiency and business outcomes. Track key metrics such as incident resolution time, alert volume, and downtime reduction. Be prepared to adjust processes as needed to ensure maximum effectiveness.
6. Foster a culture of innovation and agility: For agentic AIOps to succeed, it’s important to build a culture that values continuous improvement and agility. Encourage your team to embrace new technologies and adapt quickly to evolving needs. This mindset will optimize the value of agentic AIOps, ensuring your IT operations stay ahead of disruptions.

We all know this part — getting started is often the hardest step, especially when you’re tackling something as transformative as agentic AIOps. But here’s the thing: you can’t afford to ignore the “why” behind the change. Without a clear plan, these innovations are just shiny tools that won’t stick. Your approach matters, because how you introduce agentic AIOps to your IT infrastructure is the difference between success and just another attempt at change that doesn’t stick.

Comparing agentic AIOps to related concepts

When you’re diving into the world of IT operations, it’s easy to get lost in the sea of buzzwords. Terms like AIOps, DevOps, and ITSM can blur together, but understanding the distinctions is crucial for making informed decisions about your IT strategy. Let’s break down agentic AIOps and see how it compares to some of the most common concepts in the space.

Agentic AIOps vs. traditional AIOps

Traditional AIOps typically relies on predefined rules and static thresholds to detect anomalies or failures. When these thresholds are crossed, human intervention is often required to adjust or respond. It’s reactive at its core, often requiring manual adjustments to keep the system running smoothly.

On the other hand, agentic AIOps takes autonomy to the next level. It learns from past incidents and adapts automatically to changes in the IT environment. This means it can not only detect problems in real time but also act proactively, providing insights and recommendations without the need for manual intervention. It’s the difference between being reactive and staying ahead of potential issues before they become full-blown problems.

Agentic AIOps vs. DevOps

DevOps is all about breaking down silos between development and operations teams to speed up software delivery and improve collaboration. It focuses on automating processes in the development lifecycle, making it easier to release updates and maintain systems.

Agentic AIOps, while complementary to DevOps, adds another layer to the IT operations landscape. It enhances DevOps by automating and optimizing IT operations, providing real-time, intelligent insights that can drive more informed decision-making. Instead of just focusing on collaboration, agentic AIOps automates responses to incidents and continuously improves systems, allowing DevOps teams to focus more on innovation and less on firefighting.

Agentic AIOps vs. MLOps

MLOps focuses on managing the lifecycle of machine learning models, from training to deployment and monitoring. It’s designed to streamline machine learning processes and ensure that models perform as expected in real-world environments.

Agentic AIOps also uses machine learning but applies it in a different context. It doesn’t just manage models; it’s geared toward optimizing IT operations. By leveraging AI, agentic AIOps can automatically detect, respond to, and prevent incidents in your IT infrastructure. While MLOps focuses on the performance of individual models, agentic AIOps focuses on the larger picture—improving the overall IT environment through AI-driven automation.

Agentic AIOps vs. ITSM

ITSM (IT Service Management) is about ensuring that IT services are aligned with business needs. It focuses on managing and delivering IT services efficiently, from incident management to change control, and typically relies on human intervention to resolve issues and improve service delivery.

Agentic AIOps enhances ITSM by bringing automation and intelligence into the equation. While ITSM handles service management, agentic AIOps can automate the detection and resolution of incidents, improving efficiency and dramatically reducing resolution times. It makes IT operations smarter by predicting problems and addressing them before they impact users or business outcomes.

By comparing agentic AIOps to these related concepts, it becomes clear that it stands out as a transformative force in IT operations. While other systems may focus on specific aspects of IT management or software development, agentic AIOps brings automation, intelligence, and proactive management across the entire IT ecosystem—making it a game-changer for businesses looking to stay ahead in the digital age.

Agentic AIOps use cases

When it comes to implementing agentic AIOps, the possibilities are vast. From reducing downtime to driving proactive infrastructure management, agentic AIOps has the potential to transform IT operations across industries. Let’s dive into some specific use cases where this technology shines, showcasing how it can solve real-world problems and drive value for businesses.

Incident response and downtime reduction

One of the core strengths of agentic AIOps is its ability to detect performance degradation in real-time. When an issue arises, agentic AIOps doesn’t wait for a human to notice the problem. It immediately analyzes the situation, correlates relevant data, and generates a root cause analysis. The system can then recommend solutions to restore performance before end users are affected. In cases where downtime is minimized, the system works swiftly, ensuring minimal disruption to the business.

Predictive maintenance and asset management

Asset management can be a challenge when it comes to proactively monitoring IT infrastructure. Agentic AIOps addresses this by analyzing performance data and detecting early signs of degradation in hardware or software. By identifying these issues before they become critical, the system can suggest optimal maintenance schedules or even recommend parts replacements to prevent failures. This predictive capability helps reduce unplanned downtime and ensures smooth operations.

Security incident management

In today’s digital landscape, cybersecurity is more important than ever. Agentic AIOps plays a vital role in enhancing security by identifying unusual network activity that may indicate a potential threat. It can match this activity to known threats, isolate the affected areas, and provide step-by-step guides for IT teams to contain the threat. The system’s proactive approach reduces the likelihood of security breaches and accelerates the response time when incidents occur.

Digital transformation and IT modernization

As organizations modernize their IT infrastructure and embrace digital transformation, cloud migration becomes a key challenge. Agentic AIOps streamlines this process by analyzing dependencies, identifying migration issues, and even automating parts of the data migration process. By ensuring a smooth transition to the cloud, businesses can maintain operational continuity and achieve greater flexibility in their infrastructure.

Better customer experience

The customer experience often hinges on the reliability and performance of the underlying IT systems. Agentic AIOps monitors infrastructure to ensure optimal performance, identifying and resolving bottlenecks before they affect users. By optimizing resources and automating issue resolution, businesses can ensure a seamless user experience that builds customer satisfaction and loyalty.

Proactive infrastructure optimization

As organizations scale, managing cloud resources efficiently becomes more critical. Agentic AIOps continuously monitors cloud resource usage, identifying underutilized instances and recommending adjustments to workloads. By optimizing infrastructure usage, businesses can reduce costs, improve resource allocation, and ensure that their IT environment is always running at peak efficiency.

H3: Hybrid and multi-cloud management

For companies using hybrid or multi-cloud environments, managing a complex IT ecosystem can be overwhelming. A hybrid observability platform can gathers real-time data from on-premises systems and cloud environments, while agentic AIOps analyzes patterns, detects anomalies, and automates responses—together delivering a unified, intelligent view of the entire infrastructure. With this comprehensive visibility, organizations can optimize resources across their IT landscape and ensure that security policies remain consistent, regardless of where their data or workloads reside.

Data-driven decision making

Agentic AIOps empowers IT teams with data-driven insights by aggregating and analyzing large volumes of performance data. This intelligence can then be used for informed decision-making, helping businesses with capacity planning, resource allocation, and even forecasting future infrastructure needs. By providing actionable insights, agentic AIOps helps organizations make smarter, more strategic decisions that drive long-term success.

These use cases illustrate just a fraction of what agentic AIOps can do. From improving operational efficiency to enhancing security, this technology can bring measurable benefits across many aspects of IT management. By proactively addressing issues, optimizing resources, and providing intelligent insights, agentic AIOps empowers organizations to stay ahead of disruptions and position themselves for long-term success in an increasingly complex IT landscape.

Benefits of agentic AIOps

Let’s face it: there’s no time for fluff when it comes to business decisions. If your IT operations aren’t running efficiently, it’s not just a minor inconvenience—it’s a drain on resources, a threat to your bottom line, and a barrier to growth. Agentic AIOps isn’t just about solving problems—it’s about preventing them, optimizing resources, and driving smarter business decisions. Here’s how agentic AIOps transforms your IT landscape and delivers measurable benefits.

Improved efficiency and productivity

In an age where time is money, agentic AIOps excels at cutting down the noise. By filtering alerts and reducing unnecessary notifications, the system helps IT teams focus on what truly matters, saving valuable time and resources. It also automates root cause analysis, enabling teams to resolve issues faster and boosting overall productivity. With agentic AIOps, your IT operations become leaner and more efficient, empowering teams to act with precision.

Reduced incident risks

Every minute spent resolving critical incidents costs your business. Agentic AIOps significantly reduces response times for high-priority incidents (P0 and P1), ensuring that issues are identified, analyzed, and addressed swiftly. By preventing service disruptions and reducing downtime, agentic AIOps helps you maintain business continuity and minimize the impact of incidents on your operations.

Reduced war room time

When disaster strikes, teams often scramble into “war rooms” to fix the problem. These high-stress environments can drain energy and focus. Agentic AIOps streamlines this process by quickly diagnosing issues and providing actionable insights, reducing the need for lengthy, high-pressure meetings. With less time spent managing crises, your IT teams can redirect their focus to strategic, value-driving tasks that move the business forward.

Bridging the IT skills gap

The demand for specialized IT skills often exceeds supply, leaving organizations scrambling to fill critical positions. Agentic AIOps alleviates this challenge by automating complex tasks that once required deep expertise. With this level of automation, even teams with limited specialized skills can handle sophisticated IT operations and manage more with less. This ultimately reduces reliance on niche talent and ensures your IT team can operate at full capacity.

Cost savings

Cost control is always top of mind for any organization, and agentic AIOps delivers on this front. By automating routine tasks and improving response times, the platform helps reduce labor costs and increase overall productivity. Additionally, its ability to prevent costly outages and minimize downtime contributes to a more cost-effective IT operation, offering significant savings in the long run.

In short, agentic AIOps doesn’t just make IT operations more efficient—it transforms them into a proactive, intelligent force that drives productivity, reduces risks, and delivers lasting cost savings. In a world where the competition is fierce, this level of optimization gives organizations the edge they need to stay ahead and scale effortlessly.

How LogicMonitor enables agentic AIOps success

Let’s be honest for a moment: the path to operational excellence isn’t paved with half-measures. It’s paved with the right tools—tools that not only keep the lights on but that proactively prevent the lights from ever flickering. 

LogicMonitor is one such tool that enables agentic AIOps to thrive. By integrating observability with intelligence, LogicMonitor creates the foundation for successful AIOps implementation, making your IT operations smarter, more agile, and more efficient.

LM Envision: Comprehensive observability across hybrid environments

When it comes to achieving true agentic AIOps success, visibility is everything. LM Envision provides comprehensive, end-to-end observability across your entire hybrid IT environment. It delivers real-time data collection and analysis, empowering proactive insights that help you stay ahead of issues before they escalate. As the foundation of your agentic AIOps strategy, LM Envision enables seamless integration, providing the visibility and insights needed to optimize system performance and reduce downtime.

The scalability and flexibility of LM Envision ensures that as your business grows and IT complexity increases, your ability to monitor and manage your infrastructure does as well. Whether you’re operating on-premises, in the cloud, or in hybrid environments, LM Envision adapts, feeding your agentic AIOps system with the critical data it needs to function at peak performance. With LM Envision, you’re always a step ahead, shifting from reactive to proactive IT management and making smarter decisions based on real-time data.

Edwin AI: AI-powered incident management

In the world of agentic AIOps, speed and accuracy are paramount when it comes to incident management. That’s where Edwin AI comes in. As an AI-powered incident management tool, Edwin AI makes agentic AIOps possible by streamlining event intelligence, troubleshooting, and incident response. It automates critical processes, consolidating data from multiple sources to offer real-time incident summaries, auto-correlation of related events, and actionable insights—all while cutting through the noise.

With Edwin AI, teams no longer waste time dealing with irrelevant alerts. By filtering out the noise and presenting the most pertinent information, it speeds up incident resolution and minimizes downtime. One of its standout features is its ability to integrate with a variety of other tools, creating cross-functional visibility and enabling smarter decision-making.

Moreover, Edwin AI offers customizable models, ensuring that its insights are tailored to the unique needs of your organization. It simplifies complex technical details into plain language, enabling all team members—regardless of technical expertise—to understand the situation and take swift action. With Edwin AI, your teams can move faster, more confidently, and with greater precision, all while minimizing the risk of service disruption.

Together, LM Envision and Edwin AI form the ultimate platform for driving agentic AIOps success. By pairing observability with intelligent, autonomous incident management, these tools enable businesses to optimize operations, improve efficiency, and ultimately ensure a more proactive and resilient IT infrastructure.

Why enterprises must act now

Here’s the hard truth: if you don’t act now, you’ll fall behind. The future of IT operations is here, and it’s powered by agentic AIOps. The age of AI (GenAI) is reshaping everything, and companies that don’t harness its power risk being left in the dust.

Early adopters have the chance to redefine performance and cost efficiency. Agentic AIOps isn’t just about keeping up—it’s about staying ahead. Those who implement it today will not only meet the demands of tomorrow, they’ll shape them.

No more chasing buzzwords or empty promises. Organizations are looking for practical, scalable solutions that work. Agentic AI automates the routine so your teams can focus on what truly matters: innovation and strategic impact.

IT leaders know this: the future isn’t waiting. Adapt now or risk being irrelevant.

With the rapid growth of data, sprawling hybrid cloud environments, and ongoing business demands, today’s IT landscape demands more than troubleshooting. Successful IT leaders are proactive, aligning technology with business objectives to transform their IT departments into growth engines.

At our recent LogicMonitor Analyst Council in Austin, TX, Chief Customer Officer Julie Solliday led a fireside chat with IT leaders across healthcare, finance, and entertainment. Their insights highlight strategies any organization can adopt to turn IT complexity into business value. Here are five key takeaways:

1. Business value first: Align IT with core organizational goals

Rafik Hanna, SVP at Topgolf, emphasizes, “The number one thing is business value.” For Hanna, every tool, and every process, must directly enhance the player experience. As an entertainment destination, Topgolf’s success depends on delivering superior experiences that differentiate them from competitors and drive continued business growth. This focus on outcomes serves as a reminder for IT leaders to ask:

• How does this initiative impact our core business objectives? Every IT action should enhance the end-user experience, whether it’s for customers, clients, or internal users. At Topgolf, Hanna translates IT decisions directly to their “player experience,” ensuring every technology choice meets customer satisfaction and engagement goals.
• Are we measuring what matters? Key performance indicators (KPIs) should reflect business value, not just technical outputs. Hanna’s team, for instance, closely monitors engagement metrics to directly connect IT performance to customer satisfaction.
• Is the ROI on IT investments clear? Clear metrics and ROI assessments make the case for IT spending. For Hanna, measurable gains in customer satisfaction justify the IT budget, shifting it from a cost center to a driver of business value.

2. Streamline your toolset: Consolidate for clarity and efficiency

Andrea Curry, a former Marine and Director of Observability at McKesson, inherited a landscape of 22 monitoring and management tools—each with overlapping functions and costs. Her CTO asked, “Why do we have so many tools?” she recalls. This sparked a consolidation effort from 22 to 5 essential solutions. Curry’s team reduced both complexity and redundancy, ultimately enhancing visibility and response time.   Key lessons include:

• Inventory first: Conduct a comprehensive assessment of all current solutions and their roles. Curry’s team mapped out each tool’s purpose and cost, laying the groundwork for informed decisions.
• Eliminate redundancies: Challenge the necessity of every tool. Can one solution handle multiple functions? Curry found that eliminating overlapping tools streamlined support needs and freed resources for higher-value projects.
• Prioritize high-impact solutions: Retain tools that directly contribute to organizational goals. With fewer, more powerful tools, her team reduced noise and gained clearer insights into their environments.

3. Embrace predictive power: Harness AI for enhanced observability

With 13,000 daily alerts, Shawn Landreth, VP of Networking and NetDevOps at Capital Group, faced an overwhelming workload for this team. Implementing AI-powered monitoring leveraging LogicMonitor Edwin AI, Capital Group’s IT team cut alerts by 89% and saved $1 million annually. Landreth’s experience underscores:

• AI is a necessity: Advanced AI tools are no longer a luxury but a necessity for managing complex IT environments. For Landreth, Edwin AI is transforming monitoring from reactive to proactive by detecting potential issues early.
• Proactive monitoring matters: AI-driven insights allow teams to maintain uptime and reduce costly incidents by identifying and addressing potential failures before they escalate. This predictive capability saves time and empowers the team to focus on innovation.
• Reduce alert fatigue: AI filters out low-priority alerts, ensuring the team focuses on the critical few. In Capital Group’s case, reducing daily alerts freed up resources for high-value projects, enabling the team to be more strategic.

4. Stay ahead: Adopt new technology proactively 

When Curry took on her role at McKesson, she transitioned from traditional monitoring to a comprehensive observability model. This strategic shift from a reactive approach to proactive observability reflects the adaptive mindset required for modern IT leadership. Leaders aiming to stay competitive should consider:

• Continuously upskill: Keep pace with evolving technologies to ensure the team’s relevance and competitiveness. Curry regularly brings in training on emerging trends to ensure her team stays at the leading edge of technology.
• Experiment strategically: Curry pilots promising new technologies to assess their value before large-scale deployment. This experimental approach enables a data-backed strategy for technology adoption.
• Cultivate a culture of innovation: Foster an environment where team members feel encouraged to explore and embrace new ideas. Curry’s team has adopted a mindset of continual improvement, prioritizing innovation in their daily workflows.

5. Strategic partnerships: Choose vendors invested in your success

Across the board, our panelists emphasized the importance of strong relationships. Landreth puts it simply, “Who’s going to roll their sleeves up with us? Who’s going to jump in for us?” The right partnerships can transform IT operations by aligning vendors with organizational success. When evaluating partners, consider:

• Shared goals: A successful vendor relationship aligns with your organizational vision, whether for scalability, cost-efficiency, or innovation. Landreth’s team prioritizes vendors that actively support Capital Group’s long-term objectives.
• Proactive support: A valuable partner offers prompt, ongoing support, not just periodic check-ins. For example, Curry’s vendors provide tailored, in-depth support that addresses her team’s specific needs.
• Ongoing collaboration: Partnerships that prioritize long-term success over quick wins foster collaborative innovation. Vendors who integrate their solutions with internal processes build stronger, more effective alliances.

Wrapping up

Our panelists’ strategies—from tool consolidation to AI-powered monitoring and strategic partnerships—all enable IT teams to move beyond reactive firefighting into a proactive, value-driven approach.

By implementing these approaches, you can transform your IT organization from a cost center into a true driver of business value, turning the complexity of modern IT into an opportunity for growth and innovation.

The cloud has revolutionized the way businesses operate. It allows organizations to access computing resources and data storage over the internet instead of relying on on-premises servers and infrastructure. While this flexibility is one of the main benefits of using the cloud, it can also create security and compliance challenges for organizations. That’s where cloud governance comes in.

Cloud governance establishes policies, procedures, and controls to ensure cloud security, compliance, and cost management. In contrast, cloud management focuses on operational tasks like optimizing resources, monitoring performance, and maintaining cloud services.

Here, we’ll compare cloud governance vs. cloud management and discuss how you can use them to improve your organization’s cybersecurity posture.

What is cloud governance?

Cloud governance aims to ensure that an organization can realize the benefits of cloud computing while minimizing risks. This includes ensuring compliance with regulatory requirements, protecting data privacy, and maintaining security.

Organizations should also develop policies and procedures related to cloud services. These should be designed to meet the organization’s specific needs and be reviewed regularly.

Why is cloud governance important?

Cloud governance is important because it provides a framework for setting and enforcing standards for cloud resources. This helps ensure that data is adequately secured and service levels are met. Additionally, cloud governance can help to prevent or resolve disputes between different departments or business units within an organization.

When developing a cloud governance strategy, organizations should consider the following:

• The types of data that will be stored in the cloud and the sensitivity of that data
• The regulations that apply to the data and the organization’s compliance obligations
• The security risks associated with storing data in the cloud
• The organization’s overall security strategy
• The costs associated with using cloud services

An effective cloud governance strategy will address all of these factors and more. It should be tailored to the organization’s specific needs and reviewed regularly. Additionally, the strategy should be updated as new technologies and regulations emerge.

Organizations without a cloud governance strategy are at risk of data breaches, regulatory non-compliance, and disruptions to their business operations. A well-designed cloud governance strategy can help mitigate these risks and keep an organization’s data safe and secure.

What are the principles of cloud governance?

Cloud governance is built on principles essential for ensuring that cloud resources are used securely, efficiently, and according to business objectives. Sticking to these principles lets your organization lay out clear expectations, streamline processes, and minimize several risks associated with cloud computing. By implementing these guidelines, you can establish a robust governance framework for secure and effective cloud operations:

• Defining roles and responsibilities: Clearly define who is responsible for managing cloud services and their roles and responsibilities.
• Establishing policies and procedures: Develop policies and procedures for using cloud services, including how to provision and de-provision them, how to monitor and audit usage, and how to handle data security.
• Ensuring compliance: Make sure their cloud services comply with all relevant laws and regulations.
• Managing risk: Identify and assess risks associated with using cloud services and put controls in place to mitigate those risks.
• Monitoring and auditing: Monitor their use of cloud services and audit them regularly to ensure compliance with policies and procedures.

What is the framework for cloud governance?

A framework for cloud governance provides guidelines and best practices for managing data and applications in the cloud. It can help ensure your data is secure, compliant, and aligned with your business goals.

When choosing a framework for cloud governance, consider a few key factors:

• First, you must decide what control you want over your data and applications. Do you want complete control, or will you delegate some responsibility to a third-party provider?
• Next, you need to consider the size and complexity of your environment. A smaller organization may only need a few simple rules around data storage and access, while a larger enterprise may require a more comprehensive approach.
• Finally, you need to consider your budget. Many different cloud governance frameworks are available, so it’s essential to find one that fits within your budget.

How do you implement a cloud governance framework?

Implementing a cloud governance framework can be challenging, but it’s essential to have one in place to ensure the success of your cloud computing initiative. Here are a few tips to help you get started:

Define your goals and objectives

Before implementing a cloud governance framework, you need to know what you want to achieve. Do you want to improve compliance? Reduce costs? Both? Defining your goals will help you determine which policies and procedures need to be implemented.

Involve all stakeholders

Cloud governance affects everyone in an organization, so it’s crucial to involve all stakeholders, including upper management, IT staff, and business users. Getting buy-in from all parties will make implementing and following the governance framework easier.

Keep it simple

Don’t try to do too much with your cloud governance framework. Start small and gradually add more policies and procedures as needed. Doing too much at once will only lead to confusion and frustration.

Automate where possible

Many cloud governance tools can automate tasks such as compliance checks and cost reporting. These tools can reduce the burden on your staff and make it easier to enforce the governance framework.

What are the benefits of implementing cloud governance?

Implementing cloud governance gives organizations a structured approach to managing cloud resources, keeping cloud management aligned with business goals, and reducing risks. Using this framework, you can gain better control over your cloud environments so they operate more efficiently while safeguarding sensitive data. Cloud governance offers the following advantages for navigating the complexities of modern cloud computing.

• Improved security: One of the main concerns businesses have regarding cloud computing is security. By implementing governance structures, companies can ensure their data is safe and secure. Cloud governance can help prevent data breaches and unauthorized access to sensitive information.
• Increased transparency: Cloud governance can help increase transparency within an organization. Clear policies and procedures help ensure that everyone knows what is happening with their data, reducing the risk of fraud and corruption.
• Better resource management: Cloud governance can help businesses manage their resources better. Establishing structured guidelines allows companies to use their resources efficiently and improve overall organizational efficiency. This can help save money and improve the organization’s overall efficiency.

What is cloud management?

Cloud management is the process of governing and organizing cloud resources within an enterprise IT infrastructure. It includes the policies, procedures, processes, and tools used to manage the cloud environment.

Cloud management aims to provide a centralized platform for provisioning, monitoring, and managing cloud resources. This helps ensure that all resources are utilized efficiently and that the environment complies with corporate governance policies.

Why is cloud management important?

Cloud management is critical for businesses because it helps them optimize their cloud resources, control costs, and facilitate compliance with regulatory requirements. Cloud management tools help companies automate the provisioning, monitoring, and maintenance of their cloud infrastructure and applications.

How does cloud management work?

Organizations increasingly turn to cloud-based solutions to help them run their businesses, so it’s crucial to understand how cloud management works. By definition, cloud management is administering and organizing cloud computing resources. It includes everything from provisioning and monitoring to security and compliance.

Several different tools and technologies can be used for cloud management, but they all share a common goal: to help organizations maximize their investment in cloud computing.

The first step in effective cloud management is understanding the different types of clouds and how they can be used to meet your organization’s needs. There are three main types of clouds: public, private, and hybrid.

• Public clouds are owned and operated by a third-party service provider. They’re the most popular cloud type, typically used for applications that don’t require a high level of security or performance.
• Private clouds are owned and operated by a single organization. They offer more control and security than public clouds but are also more expensive. Private clouds are often used for mission-critical applications.
• Hybrid clouds are a mix of public and private clouds. They offer the benefits of both clouds, but they can be more challenging to manage. Organizations often use hybrid clouds to store data in both a public and a private cloud.

What is a cloud management platform?

A cloud management platform (CMP) is software that enables an enterprise to monitor and control all aspects of its cloud computing infrastructure and resources. A CMP typically provides a unified, web-based interface through which an administrator can provision, configure, and manage public and private cloud resources.

Benefits of a cloud management platform

A cloud management platform (CMP) gives you centralized control over your cloud resources, helping to optimize operations while maintaining flexibility and security. Through a combination of cloud processes and tools in a single interface, CMPs help simplify management tasks and make the platform worth the investment. Here are some key benefits that make cloud management platforms ideal for organizations that rely on modern cloud environments:

1. Cost savings: By moving your business processes and functions to the cloud, you can avoid investing in expensive on-premise software and hardware.
2. Increased efficiency: With all your business processes and functions in one place, you can streamline and automate many tasks, freeing up time for you and your team to focus on other areas of the business.
3. Improved collaboration: By sharing data and information in the cloud, you’ll provide greater transparency and visibility, allowing your team to work together more effectively.
4. Greater flexibility: With a cloud-based solution, you can access your data and applications from anywhere in the world, giving you the freedom to work from anywhere.

What are the differences between cloud governance vs. cloud management?

Cloud governance is the set of processes, policies, and controls an organization uses to manage its cloud services. Cloud management is the day-to-day operational tasks involved in managing a cloud environment, such as provisioning and configuring resources, monitoring usage and performance, and ensuring security.

The two concepts are closely related, but there are some important differences. Cloud governance is primarily concerned with setting and enforcing standards for how the cloud is used within an organization. It defines who can access which resources, what they can use them for, and how they can be configured. Cloud management, on the other hand, is focused on actually carrying out those standards on a day-to-day basis.

How do cloud governance and cloud management work together?

Organizations need both cloud governance and management to ensure that their use of cloud resources is safe, compliant, and efficient. Cloud governance provides the framework for decision-making and sets the expectations for how cloud resources will be used. Cloud management ensures that those decisions are carried out properly and resources are used as intended.

For instance, a retail company experiencing seasonal spikes in demand might develop a cloud governance framework with policies for increasing available IT resources during peak shopping periods. These policies could also help facilitate compliance with data privacy laws like GDPR and define access controls to protect sensitive customer information. Meanwhile, cloud management tools would implement these policies by allocating additional computing resources during Black Friday, monitoring usage to prevent performance issues, and decommissioning unused resources afterward to avoid unnecessary costs.

In another case, a financial institution might rely on cloud governance to establish policies that restrict access to customer financial data to authorized personnel only through multi-factor authentication mechanisms. Governance policies might also require regular compliance audits to ensure systems meet industry regulations and standards. Cloud management tools enforce these rules using automated monitoring to detect and alert administrators of unusual activity, such as unauthorized access attempts or data anomalies.

Best practices for implementing cloud governance and cloud management 

Implementing effective cloud governance and management takes careful planning and execution. By adopting best practices, you can be confident your cloud environments are secure and efficient and support your business objectives. By following these best practices, your organization can create a cloud governance and management strategy that builds long-term success in your cloud initiatives:

1. Start with a clear governance framework

Develop a comprehensive cloud governance framework that aligns with your organization’s goals and regulatory requirements. Define data storage, access control, compliance, and risk management policies. Ensure the framework addresses key areas like resource provisioning, decommissioning, and cost monitoring. 

2. Prioritize stakeholder involvement

Include stakeholders in creating a strategy that balances efficiency with risk management for all operations, such as IT, legal, compliance, and business administration. Collaboration ensures alignment, avoids siloed approaches, and encourages continued communication across all departments. 

3. Leverage automation

Implement tools that automate routine management tasks like resource provisioning, performance monitoring, and compliance checks. Automation reduces human error and helps to ensure the consistent application of policies, freeing up IT teams for higher-value tasks. 

4. Regularly monitor performance and costs

Use cloud management platforms to monitor resource utilization, performance metrics, and costs in real-time. Identify underutilized resources or unnecessary expenditures and optimize configurations to reduce waste. 

5. Enforce robust security controls 

Integrate security measures such as multi-factor authentication, encryption, and regular vulnerability assessments into your cloud management practices. Security should be a cornerstone of your governance policies, especially if your organization handles sensitive information like health or financial data. 

6. Conduct regular audits 

Routinely audit your cloud environment to make sure your system remains compliant with governance policies and industry regulations. Use these audits to identify gaps, improve policies, and validate that your management practices meet organizational goals. 

7. Document and communicate policies 

Maintain up-to-date documentation of governance and management policies, and keep them easily accessible to all relevant personnel. Conduct training sessions to train teams on these policies to keep everyone up-to-speed on current practices and requirements. 

8. Plan for growth 

Design strategies that can accommodate organizational growth. Whether you’re adding new applications or transitioning to a hybrid cloud model, ensure your framework is adaptable to future needs. 

9. Establish metrics for success 

Define key performance indicators (KPIs) to measure the effectiveness of your governance and management efforts. Example KPIs might include cost savings, compliance audit results, or system uptime metrics. 

10. Continuously review and improve 

The cloud landscape evolves rapidly — your governance and management strategies should, too. Regularly evaluate and update existing policies to keep up with emerging technologies or regulations. Incorporate lessons learned from audits or incidents into your new policies and practices.

Use cases for cloud governance and management

Cloud governance and management are critical for maintaining secure, efficient, cost-effective cloud environments. Together, they provide the oversight and operational structure necessary to address key challenges organizations face when using the cloud. From managing costs to enhancing performance, the right governance and management strategies align cloud resources with business objectives while minimizing risks. Here are some core use cases where cloud governance and management prove to be indispensable:

1. Cost control and optimization: To keep cloud costs under control, IT organizations must continuously monitor usage patterns and optimize resource utilization. They also need to identify and track wasteful spending to take corrective action. Cloud governance solutions can help by providing visibility into cost center performance, resource utilization, and spending patterns.
2. Security and compliance: To ensure that data stored in the cloud is secure, IT organizations need to implement comprehensive security controls. They also need to be able to track and monitor compliance with internal policies and external regulations. Cloud governance and management solutions can help by providing visibility into security posture and vulnerabilities.
3. Performance and availability: To ensure that applications running in the cloud are performing optimally, IT organizations must constantly monitor key metrics. They also need the ability to quickly identify and resolve performance issues. Cloud governance and management solutions can help by providing visibility into performance metrics and SLAs.

Real-world applications of cloud governance and management

Cloud governance and management tackle the distinct challenges of operating in cloud environments. From controlling costs to optimizing performance, these strategies work together to create an efficient and adaptable cloud ecosystem. By combining cloud governance and management, organizations in these industries can address their unique challenges, stay compliant, and enhance reliability:

Healthcare compliance and data protection

A healthcare provider implementing a cloud-based patient records system must meet stringent compliance requirements like HIPAA. Cloud governance establishes policies for access controls, data encryption, and compliance audits to protect patient data. Cloud management tools enforce these policies, automatically flagging unauthorized access attempts and keeping system configurations compliant with the most current regulatory standards.

Retail cost optimization during peak seasons

A retail company preparing for holiday sales uses cloud governance to set policies for scaling resources during peak demand while controlling costs. Governance frameworks dictate thresholds for provisioning additional servers or storage based on projected traffic. Cloud management tools automatically scale resources up or down, monitor performance, and decommission unused resources once demand subsides.

Financial services security and performance

A financial institution using cloud-hosted trading platforms depends on real-time data accuracy, real-time observability, and high availability. Cloud governance ensures robust policies for disaster recovery and data replication. At the same time, cloud management tools monitor system performance and uptime, sending alerts if performance metrics fall below SLA-defined thresholds.

Education sector availability and scalability

An online learning platform sees a surge in users during enrollment periods. Cloud governance defines policies to maintain application availability and enforce data privacy regulations like FERPA. Cloud management tools dynamically allocate computing power to optimize user experiences, even during high-demand periods.

The role of governance and management in the cloud

Cloud governance and cloud management are two important aspects of working with the cloud. Each has its own benefits and importance, and it is essential to understand both before deciding to move to the cloud.

The framework for cloud governance provides guidelines for working with the cloud safely and efficiently. The principles of cloud governance ensure that data security and privacy are always considered. Cloud management platforms provide an easy way to manage all your clouds from one central location, giving you more control over your data.

Maximize your cloud potential with the right strategies 

Understanding cloud governance and cloud management is key to building a secure, efficient, and scalable cloud environment. These complementary strategies will help your organization balance oversight and operations, keeping cloud resources aligned with business goals while reducing risks. Whatever your main focus, having a robust framework in place ensures you’re getting the most out of your move to the cloud. 

By recognizing the differences between these two concepts, businesses can ensure they are getting the most out of their move to the cloud. If you’re considering cloud management and governance strategies for your organization, contact us today to learn more about what this process entails and some of its benefits.

PostgreSQL and MySQL are two of the most popular open-source databases available today. They both provide the database backend for many web applications, enterprise software packages, and data science projects. The two databases share some similarities in that they both adhere to the SQL standard. 

However, some key differences might influence your decision to choose one over the other. PostgreSQL is known for its advanced features, impressive durability, and scalability. MySQL is well-known for its ease of use and speed in read/write operations.

Here’s an overview of their similarities and differences, including their architectures, data types, indexing schemes, security, and performance.

PostgreSQL and MySQL similarities

Both PostgreSQL (also known as “Postgres”) and MySQL are Relational Database Management Systems (RDBMS). That means both store data in rows and tables, have a mechanism to define the relationships between the data in the tables and provide the Structured Query Language (SQL) to access the data via standardized queries.

Both database systems are ACID-compliant. ACID (atomicity, consistency, isolation, durability) compliance ensures data consistency and integrity, even in the face of system errors, hardware failures, and power outages. Both support replication for adding more servers to host data with fault tolerance and a distributed workload. 

MySQL and PostgreSQL are both free and open source, meaning that anyone can obtain the source code, install the software, and modify it how they see fit. Both offer tight integration with web servers like Apache and programming languages like PHP and Python.

Architectural differences and data types

While both MySQL and PostgreSQL are examples of an RDBMS, PostgreSQL also qualifies as an Object-Relational Database Management System or ORDBMS. This means that Postgres has the typical characteristics of a relational database, and it’s also capable of storing data as objects.

At a high level, objects in software development are models with attributes and properties that can be accessed with forms of code known as procedures and methods.

To see the difference, look at the supported data types in both systems. MySQL supports a set of standard data types, including VARCHAR (text fields limited to a certain length), TEXT (free-form text), INTEGER (an integer number), BOOLEAN (a true/false field), and DATE (a timestamp). Meanwhile, PostgreSQL supports the standard data types and a wide range of more complex data types not seen in a traditional RDBMS. This includes MONEY (a currency amount), INET (IP addresses), MACADDR (a network device’s MAC address), and many other specialized objects.

Perhaps most importantly, Postgres supports the JSON and JSONB data types, which are JSON text and binary JSON data. As most REST web service APIs today transfer data in JSON format, PostgreSQL is a favorite among app developers and system administrators. While MySQL can be made to store JSON text, the ability to natively query stored JSON data is a major advantage of PostgreSQL.

MySQL and PostgreSQL query languages

PostgreSQL supports creating custom data models with its PL/pgSQL query language, which is substantially more full-featured than MySQL’s standard SQL implementation. 

PL/pgSQL can be seen as both a query language and a procedural programming language. PL/pgSQL supports programming constructs like loops, conditional statements, variables, and error handling. The language also makes it easy to implement user-defined functions and stored procedures in queries and scripts.

MySQL’s SQL implementation lacks these features and is best suited for simple queries, data sorting, and exporting.

Even though PL/pgSQL is unique to PostgreSQL, it actually has a stricter adherence to SQL standards than MySQL’s SQL implementation. Advanced SQL features like window functions and common table expressions (CTEs) are available in PostgreSQL but not MySQL.

Database ecosystem and tools

Both PostgreSQL and MySQL boast robust ecosystems supported by various tools and integrations that enhance their functionality and streamline database management.

PostgreSQL’s ecosystem is enriched by an extensive range of open-source and commercial tools designed for automation, scaling, sharding, and migration. Tools like pgAdmin and DBeaver provide intuitive interfaces for database management, while PgBouncer and Patroni simplify connection pooling and high-availability setups. For scaling, Citus offers advanced sharding capabilities, enabling horizontal scaling for large datasets and high traffic. Migration tools like pg_upgrade ensure seamless upgrades between PostgreSQL versions, while Ora2Pg facilitates migration from Oracle databases.

MySQL’s ecosystem is equally expansive, with tools catering to various database management needs. MySQL Workbench provides a comprehensive graphical interface for database design, administration, and performance tuning. For scaling, MySQL supports sharding through ProxySQL and Vitess, which allow for horizontal scaling and improved database performance. Percona Toolkit and AWS Database Migration Service (DMS) streamline migrations, making it easier for enterprises to transition to or from MySQL.

Both ecosystems support automation tools like Ansible and Terraform for infrastructure management, ensuring smoother deployment and scaling of database instances. Whether you choose PostgreSQL or MySQL, the ecosystems offer many tools to optimize database performance and simplify complex operations.

Indexing Methods

Indexes are crucial for database performance, speeding up data retrieval and optimizing queries. PostgreSQL and MySQL offer various indexing methods to suit different use cases:

• B-Tree Indexing: The default method in both databases, ideal for efficient data retrieval in large datasets.
• GiN & GiST Indexing: PostgreSQL-specific, designed for complex data types like arrays, JSON, and full-text search.
• R-Tree Indexing: Suitable for spatial data (points, lines, polygons), enabling faster geospatial queries.
• Hash Indexing: MySQL-specific, uses hash tables for efficient equality-based lookups but not range queries.
• Full-Text Indexing: Supports advanced text searches with keywords and phrases in both databases.

Choosing the right index type boosts query performance and ensures your database meets application demands.

PostgreSQL vs MySQL performance and scalability 

Both PostgreSQL and MySQL are capable of scaling to handle large amounts of data and high levels of traffic and to support complex applications. However, scaling MySQL typically involves adding more hardware and database instances, while PostgreSQL has some advanced features that naturally support scaling.

PostgreSQL uses a system called MVCC (Multiversion Concurrency Control) that allows multiple users to access and modify data simultaneously without locking out or slowing down each other’s queries like MySQL. This is particularly helpful for applications requiring high read/write activity levels.

When adding additional servers, MySQL uses binary log-based replications, which is fast but can lead to data inconsistencies when network hiccups interrupt replication activities. PostgreSQL uses the “log-shipping” approach, which is more reliable but can be slower than binary log replication. However, PostgreSQL also supports table partitioning, which allows a single table to be spread across multiple smaller tables. This tends to improve performance because smaller amounts of data are queried simultaneously.

PostgreSQL also has a more advanced query optimizer than MySQL, which helps execute queries more efficiently. PostgreSQL also sports a larger maximum table size than MySQL, making it better suited for applications with large datasets.

Security

PostgreSQL and MySQL take different approaches to security. Both have mechanisms for granting access to schemas and tables to defined users, but PostgreSQL offers more advanced features.

PostgreSQL has a fine-grained approach to user privileges, allowing administrators to assign more specific user privileges and roles. MySQL, however, uses a broader and more basic authorization system with a combination of user accounts and global or database-specific privileges. PostgreSQL supports many authentication methods beyond the simple username and password combination. This includes authenticating against an LDAP server or Active Directory and certificate-based authentication.

Both systems support encryption, with PostgreSQL offering more options. In particular, PostgreSQL supports column-level encryption and a feature known as Transparent Data Encryption (TDE). With TDE, all data in a schema is encrypted using a symmetric encryption key. This key, in turn, is protected by a master key that can be stored in a software key management system or a hardware-based security module.

MySQL uses SSL (Secure Sockets Layer) to help ensure data integrity, which makes it a popular database for web applications. Beyond that, MySQL doesn’t offer as many security and encryption features as PostgreSQL. But that doesn’t mean it’s insecure. A MySQL installation can be secured well enough to meet enterprise standards through the judicious use of strong passwords and network-level security.

Transactions

An RDBMS’s transaction methodology ensures data consistency and integrity while playing a large part in the database’s overall performance. The speed at which transactions are performed defines whether a database system suits a particular task.

Since both PostgreSQL and MySQL are ACID-compliant, both support transaction rollbacks and commits. However, MySQL does not enable transactions by default, opting for “auto-commit” mode out of the box. This means each SQL statement is automatically committed or rolled back unless this setting is changed.

MySQL uses a locking mechanism optimized for performance but can lead to inconsistencies in some cases. PostgreSQL uses a strict locking mechanism for a higher level of consistency.

Community support

MySQL first gained popularity in Web 1.0 days, partly because it’s open source and works well with other free and open-source software such as the PHP language and operating systems built on the Linux kernel. A strong community has built around MySQL over time, making it one of the most popular open-source packages ever. 

The well-known acronym LAMP—for Linux, Apache, MySQL, and PHP (or Perl, or Python)—came from this community in honor of the free software packages that have powered many dynamic websites for decades.

MySQL was created by Swedish developers Michael Widenius and David Axmark in 1995. A year later, the two founded the company MySQL AB to provide commercial support and consulting services for the database as it grew in popularity. In 2008, Sun Microsystems acquired MySQL AB for $1 billion. Two years later, Sun was acquired by Oracle Corporation, which means the tech giant owns MySQL.

This raised concerns in the open-source community that Oracle would prioritize its own proprietary RDBMS solutions over MySQL. These fears have mostly been unfounded, as Oracle continues to develop MySQL and offer it under the GNU General Public License (GPL), making it free for personal and non-commercial use. However, the GPL allows Oracle to charge for commercial uses of MySQL, which makes some in the community no longer consider MySQL to truly be “free and open source.”

In response to these concerns, a community-supported version of MySQL has emerged called MariaDB. While identical to MySQL in basic form and function, MariaDB lacks some of MySQL’s advanced features.

PostgreSQL is released under a modified version of the MIT license known as the PostgreSQL License. This is a permissive free and open-source license, allowing users a great deal of flexibility in how they can use and modify the software. 

As a result, PostgreSQL remains one of the most popular open-source databases in the world, with a large community support base of many users, enterprise admins, and application developers. However, there are more community contributions to the MySQL and MariaDB ecosystems.

Recent developments

Both PostgreSQL and MySQL have introduced notable updates in recent versions, keeping them at the forefront of open-source database innovation.

The release of PostgreSQL 17 in September 2024 brought several advancements. A new memory management system for the VACUUM process reduces memory consumption and improves overall performance. SQL/JSON capabilities were expanded with functions like JSON_TABLE(), enabling seamless transformation of JSON data into table formats. Logical replication has seen enhancements, such as failover control and incremental backup support via pg_basebackup. Query performance improvements include optimized handling of sequential reads and high-concurrency write operations. PostgreSQL 17 also introduced a COPY command option, ON_ERROR ignore, which enhances data ingestion workflows by continuing operations even when encountering errors.

MySQL 8.0.40, released in October 2024, continues to refine database performance and compliance. Enhancements to the InnoDB storage engine improve adaptive hash indexing and parallel query performance. Security has been bolstered with updates to OpenSSL 3.0.15 integration, ensuring compliance with modern encryption standards. The introduction of the –system-command option allows for finer control over client commands, and a revamped sys schema improves the performance of key views like innodb_lock_waits. MySQL also focuses on developer flexibility with improved error handling and broader compatibility for tools and libraries.

These ongoing developments highlight the commitment of both database communities to addressing evolving performance, scalability, and security needs, ensuring their continued relevance in diverse application environments.

Use cases

MySQL is utilized by an untold number of websites thanks in part to the database being free and open source, as well as its out-of-the-box support for the PHP language. The combination of PHP and MySQL helped create a rush of dynamic websites that didn’t have their HTML code manually updated.

Early on, Google used MySQL for its search engine. Over time, as the search giant’s dataset grew, it moved to different database technologies optimized for unstructured data and fuzzy searches. (Today, Google search is powered by Google’s own distributed data storage system, Bigtable.)

MySQL is still widely used for many small- to medium-sized web applications. Content management systems and specialized web apps like Geographic Information Systems (GIS) almost always support MySQL as a database backend.

Many enterprises also use it as the data backend for their internal applications and data warehouses. PostgreSQL is used in many of the same scenarios. Most web apps that support MySQL will also support PostgreSQL, making the choice a matter of preference for sysadmins and database administrators.

PostgreSQL pros and cons

Here are some of the pros of choosing PostgreSQL:

• Performance and scalability that matches commercial RDBMS products.
• Concurrency support for multiple write operations and reads at the same time.
• The PL/pgSQL language and support for other programming languages, such as Java, JavaScript, C++, Python, and Ruby.
• Support for high availability of services and a reputation for durability.

Some of the cons of PostgreSQL include:

• It can be complex to set up and manage, particularly for newcomers.
• Reliability comes at a performance cost.
• Large databases used in complex applications can be memory intensive.
• Less community support than MySQL/MariaDB.

MySQL pros and cons

The pros of MySQL include:

• MySQL’s storage engines enable fast performance.
• A  small footprint and an easy-to-use replication system make it easy to grow and scale.
• Open-source solid community support.
• Nearly all web applications and enterprise systems support MySQL.

Here are some cons of choosing MySQL:

• Not as scalable as PostgreSQL or newer database systems.
• Lack of advanced features like full-text search and complex data types.
• Less resilience when processing complex queries.
• There is no built-in support for backups, requiring third-party backup software.

PostgreSQL and MySQL: Which to choose?

Both PostgreSQL and MySQL are extremely capable RDBMS packages. While PostgreSQL clearly supports more advanced features and has a greater reputation for reliability, that doesn’t mean MySQL is a bad choice.

MySQL’s relative simplicity makes it a great choice for smaller and medium-sized web applications. Those new to SQL and RDBMS applications, in general, can pick up the basics of MySQL quickly, making it a great choice for enterprises with limited IT resources. MySQL also has a strong community, with decades of apps supporting MySQL.

If you will be dealing with a larger dataset or developing complex custom applications, PostgreSQL is an excellent choice. Its support for custom data types and the PL/pgSQL language make Postgres a favorite of sysadmins, web developers, and database administrators worldwide.

PostgreSQL vs MySQL: A side-by-side comparison

Choose the right database, monitor with ease

Selecting between a PostgreSQL and MySQL database ultimately depends on your specific project requirements. PostgreSQL excels in handling complex queries, large datasets, and enterprise-grade features, making it ideal for analytics, REST APIs, and custom applications. MySQL, on the other hand, shines in simplicity, speed, and compatibility, making it perfect for small-to-medium-sized applications and high-traffic web platforms.

Whatever database you choose, ensuring its performance and reliability is critical to your IT infrastructure’s success. That’s where LogicMonitor’s database monitoring capabilities come in.

Comprehensively monitor all your databases in minutes with LogicMonitor. With autodiscovery, there’s no need for scripts, libraries, or complex configurations. LogicMonitor provides everything you need to monitor database performance and health alongside your entire infrastructure—whether on-premises or in the cloud.

Why LogicMonitor for Database Monitoring?

• Turn-key integrations: Monitor MySQL, PostgreSQL, and other databases effortlessly.
• Deep insights: Track query performance, active connections, cache hit rates, and more.
• Auto-discovery: Instantly discover database instances, jobs, and dependencies.
• Customizable alerts: Eliminate noise with thresholds and anomaly detection.
• Comprehensive dashboards: Gain visibility into database and infrastructure metrics in one platform.

Ready to optimize and simplify your database management? Try LogicMonitor for Free and ensure your databases deliver peak performance every day.

Once upon a time, the prospect of an organization letting another organization manage its IT infrastructure seemed either inconceivable or incredibly dangerous. It was like someone handing their house keys to a stranger. Times have changed.

Remote Infrastructure Management (RIM) — when Company X lets Company Y, or a piece of software, monitor and manage its infrastructure remotely — has become the standard in some industries. It’s sometimes the de facto method for IT security, storage, and support. 

When did this happen? When organizations started working remotely. 

When the COVID-19 pandemic spiraled and governments issued social distancing and stay-at-home orders, companies rolled down the blinds and closed the doors. When remote IT management was a business need, not a request, CIOs came around to the idea. There was no other choice. It was that or nothing. 

The C-suite discovered what IT leaders had known for years: RIM is safe, cheap, and just as effective as in-house management. 

RIM is not perfect. There are challenges. Problems persist. So, IT leaders need to iron out the kinks before RIM becomes the standard across all industries. 

In this guide, learn the current state of RIM, then discover what the future holds.

What is remote infrastructure management?

RIM is the monitoring and management of IT infrastructure from a remote location. Company X outsources infrastructure management to Company Y, for example. Alternatively, super-smart software handles all this monitoring and management, and organizations can view management processes in real time from their devices. An administrator might need to visit the organization’s physical location (or, post-COVID, a home location) to repair broken hardware, but that should be a rare occurrence. 

The term “IT infrastructure” — the thing or things that RIM monitors and manages — has different definitions but might include one or all of the below:

• Software
• Hardware
• Data centers
• Networks
• Devices
• Servers 
• Databases
• Apps
• Emails
• Telephony
• IT services
• Customer relationship management (CRP) systems 
• Enterprise resource planning (ERP) systems 

The list goes on. 

What is the current state of remote infrastructure management?

The IT infrastructure management landscape looks completely different from 18 months ago. Back then, most IT teams took care of monitoring and management. But then the pandemic hit. Suddenly, organizations required RIM solutions for several reasons:

• IT teams, now working from home, could no longer manage IT infrastructure on-site effectively. 
• Work-from-home models presented unique security challenges that required a more scalable infrastructure management solution. Employees accessed different software on different devices at different locations, and only RIM could solve these challenges.
• As the economy stuttered, many organizations reduced IT spending, and RIM provided a cheaper solution than conventional in-house IT. 
• New technologies like teleconferencing provided additional security challenges. Hence, there is a demand for a more comprehensive infrastructure management solution. 

Recent research from LogicMonitor reveals the collective concerns of IT leaders who monitor and manage the IT infrastructure of at-home employees:

• 49% worry about dealing with internet outrages and other technical issues remotely.
• 49% think too many employees logging into systems remotely will cripple networks.
• 38% worry about employees logging into systems through virtual private networks (VPNs).
• 33% don’t have access to the hardware they need to do their jobs. 
• 28% don’t think teleconferencing software is secure enough. 

It’s no wonder, then, that so many of these IT leaders are looking for RIM solutions. 

Read more fascinating insights from LogicMonitor’s Evolution of IT Research Report.

How much infrastructure management is currently ‘remote’?

The great thing about RIM is its flexibility. Organizations can choose what they want a service provider or software to monitor and manage depending on variables such as internal capabilities and cost. Company X might want to manage its networks remotely but not its software, for example. Research shows database and storage system management are the most popular infrastructure ‘types’ monitored and managed remotely. 

Remote infrastructure management challenges

Not all RIMs are the same. CIOs and other IT leaders need to invest in a service provider or software that troubleshoots and solves these challenges:

Challenge 1: Growth and scalability

Only 39% of IT decision-makers feel ‘confident’ their organization can maintain continuous uptime in a crisis, while 54% feel ‘somewhat confident,’ according to LogicMonitor’s report. These professionals should seek an RIM solution that scales at the same rate as their organization.

There are other growth solutions for IT leaders concerned about uptime in a crisis. Streamlining infrastructure by investing in storage solutions such as cloud services reduces the need for hardware, software, and other equipment. With more IT virtualization, fewer problems will persist in a crisis, improving business continuity. 

Challenge 2: Security

Security is an enormous concern for organizations in almost every sector. The pandemic has exasperated the problem, with the work-from-home model presenting security challenges for CIOs. There were nearly 800,000 incidents of suspected internet crime in 2020 — up 300,000 from the previous year — with reported losses of over $4 billion. Phishing remains the No.1 cybercrime.

CIOs need a RIM solution that improves data security without affecting employee productivity and performance. However, this continues to be a challenge. IT virtualization doesn’t eliminate cybercrime, and not all service providers and software provide adequate levels of security for data-driven teams. 

There are several security frameworks to consider. IT leaders require a RIM solution that, at the least, adheres to SOC2 and ISO standards, preferably ISO 27001:2013 and ISO 27017:2015 — the gold standards of IT security. Other security must-haves include data encryption, authentication controls, and access controls. 

Then there’s the problem of data governance. When moving data to a remote location, data-driven organizations must adhere to frameworks like GDPR, HIPAA, and CCPA. Otherwise, they could face expensive penalties for non-compliance. 

Challenge 3: Costs

The cost of RIM remains a bugbear for many CIOs. As RIM is still a relatively new technology, some service providers charge larger organizations hundreds of thousands to manage and monitor hardware, software, networks, and servers. 

Investing in monitoring software provides more value for money. These programs do nearly everything a RIM services provider does but without the expensive price tag. Service providers use software to automate monitoring and management, so organizations won’t notice a big difference.

Regardless of whether organizations choose a service provider or monitoring software, the costs of both methods should provide an investment return. Research shows the average cost of a data breach in the U.S. is $8.46 million, so if remote monitoring and management prevent a breach, it’s well worth it. 

Challenge 4: Automation

As mentioned above, software automates much of remote monitoring. However, some monitoring and management tools are better at executing this process than others. That’s because RIM is still a new technology, and some vendors are working out the fine details. Regardless, monitoring tools are becoming more sophisticated daily, automating nearly all the manual processes associated with infrastructure management, such as network performance updates and security patch installation.

Challenge 5: AI/Machine learning

RIM has struggled with AI and machine learning, but this is changing fast. The best tools take advantage of these technologies by providing end-users with invaluable insights into every aspect of their IT infrastructure, from server uptime to network memory.

AI-driven tools leverage predictive analytics to analyze historical data, identify patterns, and predict potential failures before they occur, enabling IT teams to take proactive measures and prevent incidents. Machine learning enhances intelligent automation by optimizing tasks such as resource allocation and network performance, reducing the need for manual intervention and increasing overall efficiency.

AI-powered algorithms will continuously monitor your systems, detecting unusual behaviors or anomalies that could indicate security threats or performance issues, allowing for a swift response. Capacity planning is also improved as AI tools analyze infrastructure usage trends and provide recommendations for resource optimization, ensuring scalability while avoiding unnecessary costs.

Finally, machine learning models correlate data across diverse systems to generate actionable insights, helping CIOs make informed decisions, prioritize tasks, and allocate resources more effectively. These advancements are transforming RIM into a smarter, more efficient approach to infrastructure management.

Not all remote management tools use these technologies, so CIOs and software procurement teams should research the market and find the best platforms and RIM service providers. 

Challenge 6: Cloud

RIM and the cloud are a match made in technological heaven. With IT virtualization, CIOs can manage much of their infrastructure (and data) in a cloud environment, which provides these remarkable benefits:

• The cloud reduces the amount of physical hardware (on-premise hardware) in an organization.
• It safeguards data for security and governance purposes.
• Team members can access data remotely wherever they are in the world.
• It protects the environment. 
• It improves energy efficiency. 
• It scales better.
• It provides cost savings. 

The move to full virtualization won’t happen anytime soon, with many business leaders still skeptical about the cloud. 74% of IT leaders think 95% of public, private, and hybrid workloads will run in the cloud in the next five years, according to LogicMonitor’s report. 22% think it will take six years or more; 2% don’t believe it will ever happen. Still, more organizations are using the cloud than ever before. 

The cloud brings security challenges for IT teams, but the right tools will ease any concerns. 

How to implement remote infrastructure management services effectively

Implementing RIM successfully requires a structured approach that aligns with your organization’s needs, infrastructure, and goals. Below are actionable steps to ensure effective adoption:

1. Assess organizational needs

Before implementing RIM, identify what infrastructure components need to be managed remotely. This might include:

• Networks and servers for real-time monitoring
• Applications requiring frequent updates
• Critical data is subject to compliance regulations

Consider existing IT capabilities and pinpoint areas where RIM can add the most value, such as improving uptime or reducing costs.

2. Choose the right tools and providers

Select tools or service providers that match your infrastructure’s complexity and scalability requirements. Look for:

• Automation and AI capabilities to reduce manual effort
• Compliance with SOC2, ISO 27001, and other relevant standards
• Flexible pricing models that suit your organization’s budget

Ensure your chosen solution integrates seamlessly with existing systems, including hybrid and multi-cloud environments.

3. Prioritize security

Cybersecurity is a critical consideration for any RIM strategy. Implement:

• Data encryption to protect sensitive information
• Role-based access controls to minimize security risks
• Regular audits to ensure compliance with regulations like GDPR or CCPA
  Security protocols should safeguard data without hampering employee productivity

4. Leverage automation and AI

Automating routine tasks such as performance monitoring and incident detection streamlines IT and business operations. Use tools that:

• Offer predictive analytics for proactive maintenance
• Automatically apply security patches and updates

This reduces downtime and frees up IT resources for strategic initiatives.

5. Plan for scalability

As your organization grows, your RIM strategy should scale accordingly. Opt for solutions that support:

• Increasing workloads without impacting performance
• Flexible infrastructure that adapts to changing needs, such as expanding cloud storage

Scalability ensures your IT operations remain efficient during growth.

6. Train your IT teams

Equip IT staff with the skills needed to manage RIM tools effectively. Training ensures:

• Seamless tool adoption
• Improved collaboration between teams managing on-premises and remote infrastructure

A well-trained team is critical for realizing the full benefits of RIM.

7. Monitor and optimize continuously

RIM implementation doesn’t end after setup. Continuously track key performance metrics, such as:

• System uptime and availability
• Incident response times
• Infrastructure costs

Use these insights to refine your strategy and improve efficiency.

RIM vs DCIM software

While RIM and Data Center Infrastructure Management (DCIM) software share overlapping goals, they are distinct in their approach and scope. Both focus on improving visibility and control over IT infrastructure, but each caters to different operational needs.

What is DCIM software?

DCIM software specializes in managing the physical components of data centers, such as power, cooling, and space utilization. It provides insights into infrastructure efficiency and helps data center operators optimize performance, reduce energy costs, and plan for future capacity needs.

How RIM differs from DCIM

1. Scope of management
   • RIM: Broadly encompasses remote monitoring and management of IT infrastructure, including software, hardware, servers, and networks, often across multiple geographic locations.
   • DCIM: Primarily focuses on the physical aspects of a data center, such as racks, power distribution, and environmental conditions.
2. Location
   • RIM: Extends management capabilities beyond the data center, making it ideal for hybrid, remote, and multi-cloud environments.
   • DCIM: Typically operates within the confines of a physical data center, offering on-premises insights.
3. Key technologies
   • RIM: Leverages automation, AI, and cloud-based tools to provide real-time monitoring and incident management.
   • DCIM: Relies on sensors, physical monitoring tools, and predictive analytics for maintaining data center health and efficiency.
4. Use cases
   • RIM: Ideal for organizations with distributed infrastructure needing centralized, remote oversight.
   • DCIM: Suited for enterprises managing large-scale, on-premises data centers requiring detailed physical infrastructure management.

When to use RIM or DCIM

Organizations that rely heavily on hybrid IT environments or need to support remote operations benefit from RIM’s flexibility. However, for businesses with significant investments in physical data centers, DCIM provides unparalleled insights into physical infrastructure performance.

Can RIM and DCIM work together?

Yes. These solutions complement one another, with RIM focusing on the IT layer and DCIM ensuring optimal physical conditions in the data center. Together, they provide a holistic view of infrastructure performance and health.

What is the future for remote infrastructure management?

More organizations are investing in RIM. Experts predict the global RIM market will be worth $54.5 billion by 2027, growing at a CAGR of 9.7% from now until then. Meanwhile, database management and storage system management will grow at CAGR rates of 10.4% and 10% over the next seven years. The two countries that will invest the most money in RIM during this same period will be China and the United States.

With such explosive growth, expect more RIM innovations in the next few years. The software will become smarter. Service providers will offer more infrastructure services. Full cloud monitoring may exist if all infrastructure moves to the cloud. 

RIM could also trickle down to smaller businesses that still rely on manual processes for monitoring and management — or don’t carry out these critical tasks at all. As the costs of data centers, servers, and resources rise, small business owners will keep a closer eye on monitoring tools that provide them with insights such as network and bandwidth usage and infrastructure dependencies. 

Take control of your IT infrastructure today

RIM has existed, in one form or another, for several years. However, the growing demands of work-from-home have brought remote monitoring and management into the spotlight. Whether it comes from software or a service provider, RIM takes care of software, hardware, server, and network tasks organizations don’t have the time for or don’t want to complete. Despite some challenges, the future of RIM looks bright, providing busy teams with bespoke monitoring and management benefits they can’t find anywhere else.

LogicMonitor is the cloud-based remote monitoring platform for CIOs and IT leaders everywhere. Users get full-stack visibility, world-class security, and network, cloud, and server management tools from one unified view. Welcome to the future of remote monitoring. Learn more or try LogicMonitor for free.

Developers are increasingly using Kubernetes’ open-source platform to manage containerized workloads and services. Kubernetes containers became popular because it was impossible to define a resource boundary for multiple applications in a traditional CPU environment, and resource misuse created an inefficient environment.

Kubernetes solves the problem by allowing applications to work as isolated containers inside a single operating system. These lightweight containers have their own filesystem, CPU share, memory, storage space, etc. You can move the container across clouds and OS distributions, which makes them a powerful resource. Currently, there are three different Kubernetes certifications that you can take to develop your knowledge and skills even further. Keep reading to see which one is right for you.

Why should I get Kubernetes certifications?

Kubernetes certifications create new opportunities for career growth. A recent survey by Cloud Native Computing Foundation suggests that Kubernetes is the go-to choice for more than 78% of organizations, and nearly 84% of companies run containers in production. Such trends are also visible in the 2021 Red Hat OpenShift report, which states that more than half of IT companies intend to increase the use of containers in the future.

Many organizations shortlist employment candidates who hold the Kubernetes certification, so getting certified helps you stand out and often means less competition when you’re looking for a new job. Companies are also willing to pay more to K8s engineers because hiring managers realize that very few individuals are skilled in this emerging field.

Kubernetes certifications paths

The Linux Foundation manages Kubernetes certification. There are currently five certifications. These are:

• Certified Kubernetes Application Developer (CKAD)
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Security Specialist (CKS)
• Kubernetes and Cloud Native Security Associate (KCSA)
• Kubernetes and Cloud Native Associate (KCNA)

Developer path: As the name suggests, a developer builds and manages Kubernetes applications. You will design, build, and configure apps. Developers can define the resources that applications will use and troubleshoot relevant issues.

Administrative path: The administrative path focuses on managing the Kubernetes environment. Administrators may install, manage, and configure production-grade Kubernetes clusters. They’re the people behind the Kubernetes operations.

The administrative path also leads to certification as a Kubernetes Security Specialist. The CKS certification ensures best practices and covers the necessary skills to secure container-based apps and platforms from threats. It is important to note that you must hold a Certified Kubernetes Administrator license before pursuing a Kubernetes Security Specialist license.

Foundational cloud-native path: This path is designed for beginners and professionals seeking to understand cloud-native ecosystems. The KCNA certification validates knowledge of Kubernetes fundamentals, while the KCSA certification focuses on cloud-native security principles, making them excellent starting points for a cloud-native career.

Certified Kubernetes Application Developer (CKAD)

The Certified Kubernetes Application Developer exam is developed by Linux Foundation and the Cloud Native Computing Foundation. It’s a two-hour online exam that tests the candidate’s ability to perform the responsibilities of a Kubernetes developer. The two-hour exam is the first step for many new individuals.

Prerequisites

There is no prerequisite to take CKAD; however, prior experience in an IT field will help candidates easily grasp the concepts. The exam will not test candidates on material related to container runtimes and microservice architecture, but it assumes that you should know these contents.

To pass the exam, you should be comfortable with the following:

• An OCI-Compliant container Runtime.
• Concepts and architectures related to Cloud Native application.
• Knowledge of programming languages such as Java, Python, and Node.js.

Content

The course content consists of seven domains and competencies. These include core concepts, configuration, multi-container pods, observability, pod design, service and networking, and state persistence.

The exam

For the CKAD exam, candidates must score 66% or above to become certified. The exam consists of performance-based tasks that candidates must solve in a command line. Each test is proctored online using audio, video, and screen-sharing feeds, allowing the examiner to view candidates’ desktops.

It will cost you $300 to take the Certified Kubernetes Application Developer exam, but you may be eligible for a bundled discount when opting for training and the exam. The certification is valid for three years.

Certified Kubernetes Administrator (CKA)

Certified Kubernetes Administrator certificate ensures you can install, configure, and manage production-grade Kubernetes clusters. After passing the exam, you also become eligible to take the CKS exam.

Prerequisites

Although this certificate does not have prerequisites, candidates should preferably have prior experience in the IT field. The exam is designed for Kubernetes administrators, IT professionals, and cloud administrators.

To pass the exam, candidates should be comfortable in:

• Understanding the key concepts of Kubernetes networking, storage, security, and maintenance.
• Establishing basic use cases for end-users.
• Knowledge of application lifecycle, troubleshooting, and API object primitives.

Content

The exam question tests candidates’ knowledge of five key subjects. Nearly 40% of the content covers storage and troubleshooting. Another 15% is dedicated to workloads and scheduling. Cluster architecture, installation, and configuration comprise almost 25% of the exam’s questions. The remaining 20% tests your knowledge of services and networking.

The exam

You must score at least 66% to pass the CKA exam. The exam is proctored online, and you can review documents installed by the distribution. Candidates can also review the exam content instructions presented in the command line terminal. The Procter will allow you to open one additional tab on the Chrome browser to access particular online assets.

The cost of the exam is $300, which includes a free retake. The certification is valid for three years. You will receive the result within 36 hours after the completion of the exam.

Certified Kubernetes Security Specialist (CKS)

The two-hour exam for Certified Kubernetes Security Specialist evaluates candidates based on the best practices required to secure the Kubernetes environment. To pass the exam, candidates must demonstrate knowledge of securing container-based applications and the Kubernetes platform during build, deployment, and runtime.

Prerequisites

To sit in the exam, you need to pass the Certified Kubernetes Administrator exam first. You may purchase the CKS certification before the actual exam, but you can only take the exam after completing the prerequisite.

Here are some of the important points to grasp before the exam:

• Understand a broad range of best practices required to secure a Kubernetes environment.
• Basic knowledge of dealing with Kubernetes and cloud security issues in a real-world environment.
• Ability to set up clusters, run security audits, detect threats, and do static analysis.

Content

The exam is divided into six modules. The cluster setup comprises 10% of the overall content, while cluster hardening and system hardening make up 30%. The remaining 60% evaluates supply chain security, microservice vulnerability, and managing runtime security.

The exam

The exam, which consists of 15 to 20 performance-based tasks, costs $300 to register for. During the exam, you can access Kubernetes documentation, tools, and app armor. Unlike the two other certifications, CKS certification is valid for two years.

Certified Kubernetes and Cloud Native Security Associate (KCSA)

The Kubernetes and Cloud Native Security Associate (KCSA) exam is designed by the Linux Foundation to validate foundational cloud-native security skills. It serves as a starting point for those new to Kubernetes security or cloud-native technologies. The exam evaluates a candidate’s understanding of Kubernetes security concepts, cloud-native infrastructure, and industry best practices.

Prerequisites

There are no formal prerequisites for the KCSA exam. However, having a basic understanding of Kubernetes and IT security concepts can be helpful.

To pass the exam, candidates should be comfortable in:

• Understanding Kubernetes cluster components and their security features.
• Applying security fundamentals for cloud-native environments.
• Managing container security using best practices.

Content

The exam is divided into six modules. The overview of cloud-native security accounts for 14% of the content, while Kubernetes cluster component security and Kubernetes security fundamentals each comprise 22%. The Kubernetes threat model and platform security cover 16% each, and compliance and security frameworks comprise the remaining 10%.

The exam

The KCSA exam costs $250 and is an online, proctored, multiple-choice test. Candidates have 12 months from the purchase date to schedule and complete the exam. Two exam attempts are included. The certification is valid for three years.

Certified Kubernetes and Cloud Native Associate (KCNA)

The Kubernetes and Cloud Native Associate (KCNA) exam is designed by the Linux Foundation to validate foundational knowledge of Kubernetes and the wider cloud-native ecosystem. It is an entry-level certification for those new to cloud-native technologies, providing a strong starting point for IT professionals and developers.

Prerequisites

The KCNA exam has no prerequisites, making it accessible to beginners and IT professionals who want to develop cloud-native skills.

To pass the exam, candidates should be comfortable in:

• Understanding Kubernetes fundamentals and container orchestration.
• Recognizing cloud-native architecture principles.
• Applying cloud-native observability and application delivery practices.

Content

The exam is divided into five modules. Kubernetes fundamentals account for 46% of the content, container orchestration makes up 22%, and cloud-native architecture covers 16%. Cloud-native observability and application delivery account for 8% of the total.

The exam

The KCNA exam costs $250 and is an online, proctored, multiple-choice test. Candidates have 12 months from the purchase date to schedule and complete the exam, with one free retake included. Like the CKS certification, the KCNA certification is only valid for two years.

Kubernetes certifications comparison table

Certified Kubernetes Administrator (CKA) vs. Certified Kubernetes Application Developer (CKAD)

Many people need clarification about the two certifications. Because of their relevancy and similarities, they can’t decide which certification to pursue. Here’s our take on the subject.

If you have basic app development experience or are new to Kubernetes, starting as a Certified Kubernetes Application Developer may be better. The certification mainly tests your cloud-native developer and DevOps skills. In contrast, the Certified Kubernetes Administrator exam requires a thorough knowledge of the entire Kubernetes infrastructure and Linux system.

While both exams test various similar features, the Certified Kubernetes Administrator takes it up a notch by evaluating your problem-solving skills in installing, troubleshooting, maintaining, and upgrading. It also means that getting CKAD certification may be a better approach for anyone relatively new to the Kubernetes environment.

Additional cloud native certifications

As the cloud-native ecosystem continues to expand, several certifications complement Kubernetes expertise by focusing on specific cloud-native technologies. These certifications enable IT professionals to deepen their knowledge in specialized areas such as monitoring, service mesh, and cloud-native application delivery.

Prometheus Certified Associate (PCA)

The Prometheus Certified Associate (PCA) certification validates a candidate’s knowledge of observability and monitoring using Prometheus. This exam covers Prometheus fundamentals, querying with PromQL, and setting up alerts and dashboards.

Istio Certified Associate (ICA)

The Istio Certified Associate (ICA) certification focuses on the Istio service mesh, emphasizing service discovery, traffic management, and microservice security. It is ideal for developers and operators of microservice-based applications.

Other cloud native certifications

• Helm Certified Associate: Focuses on using Helm to simplify Kubernetes application deployment and management. Candidates learn about creating, managing, and maintaining Helm charts and handling Helm releases and repositories.
• Fluent Certified Associate: Emphasizes centralized logging, data processing, and log aggregation in cloud-native environments. Candidates learn about configuring Fluentd for log collection, filtering, and forwarding in distributed systems.
• Envoy Certified Associate: Validates expertise in using Envoy proxy for securing, managing, and routing microservice traffic. Candidates learn about configuring Envoy for service discovery, load balancing, traffic routing, and applying security policies such as TLS encryption and access control.

What are the overall benefits of Kubernetes certification?

Containers and the cloud are rapidly changing the IT landscape. Besides a potential rise in pay, new career opportunities, and respect from your peers, Kubernetes certifications allow everyone to integrate the newly acquired knowledge into their existing environment.

The certification allows developers to create container-based management systems. Kubernetes’s flexible environment enables developers to use a variety of programming languages and frameworks to strengthen the existing cloud infrastructure.

Operations can use Kubernetes to bridge the gap between developers and users who are not adept at learning all the scripts and tools. The team can use the technology and expertise gained from certifications to package an application with its required infrastructure.

Security professionals can use Kubernetes and containers to increase the development speed while keeping everything secure. The end-to-end toolchain supporting the existing cloud-native infrastructure creates an attack surface, which is often challenging to defend. Kubernetes can help solve this problem.

How to prepare for Kubernetes exams

A few essential tips will come in handy when preparing for Kubernetes exams:

• You can review the exam guide on the Linux Foundation’s official website. It will help you get the latest updates in each domain.
• Don’t ignore the significance of hands-on training even if you have prior experience and know-how of Kubernetes. Competent training instructors can also answer unresolved queries.
• Master the Linux command line and practice problems using a text editor. Learn systems services because the exam environment relies on them.
• Get used to the exam console as it is different from stand-alone platforms. Shortcuts that work on other platforms may not work here.
• You should also learn how to set up and administer a cluster from scratch. Various online resources can help you do it.
• Maintain your speed by memorizing where to find specific topics in the documentation. Since you only need to solve two-thirds of the questions, skip time-consuming questions and solve them if you have the time.

Taking the next step

Achieving Kubernetes certifications and learning Kubernetes skills can transform your IT career by enhancing your technical expertise, boosting your resume, and opening up exciting job opportunities. Whether you’re just starting with Kubernetes or seeking advanced cloud-native security skills, these certifications validate your capabilities and set you apart in the tech industry.

Take the next step in managing your Kubernetes environment with LogicMonitor’s container monitoring solution. Our scalable, dynamic monitoring platform provides real-time visibility into your Kubernetes and Docker applications, automatically adapting to changes in containerized resources. Identify and resolve performance issues quickly while focusing on innovation.

This managed AWS Kubernetes service scales, manages, and deploys containerized applications. Through EKS, you can run Kubernetes without installing or operating a control plane or worker nodes — significantly simplifying Kubernetes deployment on AWS.

So what does it all mean? What is the relationship between AWS and Kubernetes, what are the benefits of using Kubernetes with AWS, and what are the next steps when implementing AWS EKS? Let’s jump in.

Importance of container orchestration  

Container orchestration automates container movement, supervision, expansion, and networking. It can be used in every scenario where containers are used and will help you position the same applications across different environments. Today, Kubernetes remains the most popular container orchestration platform offered by Amazon Web Services (AWS), Google Cloud Platform, IBM Cloud, and Microsoft Azure.

As companies rapidly expand, the number of containerized applications they use also increases. However, managing them in larger quantities can become challenging. You’ll benefit from this process if your organization manages hundreds or thousands of containers. Data shows approximately 70% of developers use container orchestration tools. 

Due to its automation properties, container orchestration greatly benefits organizations. It reduces manhours, the number of employees needed, and the financial budget for containerized applications. It can also enhance the benefits of containerization, such as automated resource allocation and optimum use of computing resources.

An overview of Kubernetes

Often called K8s, Kubernetes is an open-source container orchestration tool and industry standard. Google developed this system to automate containerized applications- or microservices- development, management, and scaling. This platform was created for several reasons but was primarily developed with optimization in mind. Automating many DevOps processes, which developers once handled manually, has significantly simplified the work of software developers, allowing them to focus on more pressing, complex tasks.

Based on its applications, Kubernetes is the fastest-growing project in open-source software history after Linux. Data shows that from 2020 to 2021, the number of Kubernetes engineers skyrocketed by 67%, reaching 3.9 million. This figure represents 31% of all backend developers.

One of the main reasons Kubernetes is so popular is the increasing demand for businesses to support their microservice architecture. Kubernetes makes apps more flexible, productive, and scalable by providing load balancing and simplifying container management. 

Other benefits include:

• Container orchestration savings: Once Kubernetes is configured, apps run with minimal downtown while performing well.
• Increased efficiency among DevOps teams, allowing for faster development and deployment times.
• The ability to deploy workloads across several cloud services.
• Since Kubernetes is an open-source tool and community-led project, there is strong support for continuous improvement and innovation. A large ecosystem of tools has been designed to use with this platform.

What is EKS?

Data shows that of those running containers in the public cloud, 78% are using AWS, followed by Azure (39%), GCP (35%), IBM Cloud (6%), Oracle Cloud (4%), and Other (4%). AWS remains the dominant provider. 

AWS offers a commercial Kubernetes service — Amazon Elastic Kubernetes Service (EKS). This managed service allows you to run Kubernetes on AWS and on-premises, benefiting from the vast number of available services. By integrating with AWS services, you’ll benefit from supply scalability and security for your applications. For example, IAM is used for reliability, Elastic Load Balancer for load distribution, and Amazon ECR for container image. 

Adding a system like AWS EKS allows you to run Kubernetes applications on various systems, like AWS Fargate. Along with benefiting from greater performance, scalability, and reliability, you can integrate with AWS networking and security services such as AWS Virtual Private Cloud. It will enhance your Kubernetes system, which will optimize your business overall.  

AWS EKS can help you gain greater control over your servers or simplify cluster setup. 

Amazon EKS functionality

Amazon EKS simplifies Kubernetes management by handling the control plane while giving users flexibility over worker node configurations. Its architecture is designed for scalability, reliability, and seamless integration with the AWS ecosystem.

1. Core architecture

Amazon EKS operates through two primary components: the Kubernetes control plane and worker nodes.

• Kubernetes control plane: This plan is managed entirely by AWS and includes Kubernetes API servers and management services spread across multiple AWS Availability Zones, ensuring high availability.
• Worker nodes: These are deployed within a customer’s Amazon VPC, allowing full administrative control over scaling, upgrades, and security configurations.

2. Deployment options

Amazon EKS supports several deployment models to meet varying business needs:

• Managed node groups: AWS provisions, scales, and automatically manages worker nodes.
• Self-managed nodes: Users deploy and manage their own worker nodes with complete customization.
• Fargate: Serverless Kubernetes deployment where AWS manages both the control plane and the underlying infrastructure, enabling container execution without EC2 instances.
• Hybrid deployments: Kubernetes clusters can be extended to on-premises infrastructure using Amazon EKS Anywhere.

3. AWS service integrations

Amazon EKS integrates with a broad range of AWS services for enhanced functionality:

• Networking: Amazon VPC provides isolated networking environments, Elastic Load Balancing ensures traffic distribution, and AWS PrivateLink secures data exchange.
• Storage: Amazon EBS is used for persistent storage, Amazon S3 is used for object storage, and Amazon EFS is used for file storage.
• Security: IAM manages user access, AWS Key Management Service (KMS) secures sensitive data, and AWS Shield protects against DDoS attacks.
• Monitoring and logging: Amazon CloudWatch collects performance metrics, AWS CloudTrail tracks activity logs, and AWS X-Ray provides distributed tracing.

How does AWS EKS work with Kubernetes?

AWS EKS supplies an expandable and available Kubernetes control panel. For optimum performance, it runs this control panel across three availability zones. AWS EKS and Kubernetes collaborate in several different areas to ensure your company receives the best performance.

1. AWS Controller lets you manage and control your AWS service from your Kubernetes environment. Using AWS EKS, you can simplify building a Kubernetes application.
2. EKS can integrate with Kubernetes clusters. Developers can use it as a single interface to organize and resolve issues in any Kubernetes application implemented on AMS.
3. EKS add-ons are pieces of operational software. These add-ons will increase the functionality of Kubernetes operations. When you start an AMS cluster, you can select any applicable add-ons. Some of these add-ons include Kubernetes tools for networking and AWS service integrations.

Benefits of AWS EKS over standalone Kubernetes

There are several benefits of AWS EKS when compared to native Kubernetes.

• Implementing AWS EKS will remove time-consuming processes like creating the Kubernetes master cluster. With standalone Kubernetes, your employees would have to spend many company hours designing and building different infrastructures.
• AMS EKS eliminates a singular point of failure as the Kubernetes control plane is spread across various AWS availability zones.  
• EKS is embedded with a range of AWS monitoring services, which means it has scalability and can grow as your company expands. It makes features like AWS Identity Access Management and Elastic Load Balancing straightforward and convenient for your employees.

Amazon EKS use cases

Amazon EKS supports a variety of enterprise use cases, making it a versatile platform for running containerized applications. Below are some of the most common applications where Amazon EKS excels:

1. Deploying in hybrid environments

Amazon EKS enables consistent Kubernetes management across cloud, on-premises, and edge environments. This flexibility allows enterprises to run sensitive workloads on-premises while leveraging cloud scalability for other applications.

2. Supporting machine learning workflows

Amazon EKS simplifies the deployment of machine learning models by enabling scalable and efficient data processing. Frameworks like TensorFlow and PyTorch can run seamlessly on EKS, with access to AWS services like Amazon S3 for data storage and AWS SageMaker for model training and deployment.

3. Building web applications

Web applications benefit from Amazon EKS’s automatic scaling and high availability features. EKS supports microservices-based architectures, allowing developers to build and deploy resilient web applications using services such as Amazon RDS for databases and Amazon ElastiCache for caching.

4. Running CI/CD pipelines

Development teams can use Amazon EKS to build and manage CI/CD pipelines, automating software release processes. Integration with tools like Jenkins, GitLab, and CodePipeline ensures continuous integration and deployment for modern applications.

Amazon EKS best practices

To ensure smooth operation and maximum efficiency when managing Amazon EKS clusters, following best practices centered around automation, security, and performance optimization is essential. These practices help minimize downtime, improve scalability, and reduce operational overhead.

1. Automate Kubernetes operations

Automation reduces manual intervention and increases reliability. Infrastructure-as-code tools like Terraform or AWS CloudFormation can be used to define and deploy clusters. CI/CD pipelines can streamline code deployment and updates. Kubernetes-native tools like Helm can be used for package management, and ArgoCD can be used for GitOps-based continuous delivery.

2. Strengthen security

Securing your Kubernetes environment is crucial. Implement the following security best practices:

• Access control: Use AWS Identity and Access Management (IAM) roles and policies to manage access rights.
• Network security: Enable Amazon VPC for isolated network environments and restrict inbound/outbound traffic.
• Data encryption: Use AWS Key Management Service (KMS) for data encryption at rest and enforce TLS for data in transit.
• Cluster hardening: Regularly update Kubernetes versions and EKS node groups to apply the latest security patches.

3. Optimize cluster performance

Performance optimization ensures workloads run efficiently without overspending on resources. Consider the following strategies:

• Auto-scaling: Enable Kubernetes Cluster Autoscaler to adjust the number of worker nodes based on demand automatically.
• Right-sizing resources: Use AWS Compute Optimizer to recommend the best EC2 instance types and sizes.
• Monitoring and logging: Amazon CloudWatch and AWS X-Ray are used to monitor and trace application performance.

AWS EKS operation

AWS EKS has two main components — a control plane and worker nodes. The control plane has three Kubernetes master nodes that will be installed in three different availability zones. It runs on the cloud controlled by AMS. You cannot manage this control panel directly; it is managed through AMS.

The other component is worker nodes, which run on the organization’s private cloud and can be accessed through Secure Shell (SSH). The worker nodes control your organization’s containers, and the control panels organize and monitor the containers’ creation and place of origin.

As EKS operations are flexible, you can position an EKS cluster for every organization or use an EKS cluster from multiple applications. Without EKS, you would have to run and monitor the worker nodes and control panel, as it would not be automated. Implementing an EKS operation frees organizations from the burden of operating Kubernetes and all the infrastructure that comes with it. AWS does all the heavy lifting.

Here is how to get started with AWS EKS.

Amazon EKS pricing

Understanding Amazon EKS pricing is essential for effectively managing costs. Pricing is determined by various factors, including cluster management, EC2 instance types, vCPU usage, and additional AWS services used alongside Kubernetes.

Amazon EKS cluster pricing

All Amazon EKS clusters have a per-cluster, per-hour fee based on the Kubernetes version. Standard Kubernetes version support lasts for the first 14 months after release, followed by extended support for another 12 months at a higher rate.

Amazon EKS auto mode

EKS Auto Mode pricing is based on the duration and type of Amazon EC2 instances launched and managed by EKS Auto Mode. Charges are billed per second with a one-minute minimum and are independent of EC2 instance purchase options such as Reserved Instances or Spot Instances.

Amazon EKS hybrid nodes pricing

Amazon EKS Hybrid Nodes enable Kubernetes management across cloud, on-premises, and edge environments. Pricing is based on monthly vCPU-hour usage and varies by usage tier.

Other AWS services pricing

When using Amazon EKS, additional charges may apply based on the AWS services you use to run applications on Kubernetes worker nodes. For example:

• Amazon EC2: For instance capacity
• Amazon EBS: For volume storage
• Amazon VPC: For public IPv4 addresses

AWS Fargate pricing: Charges are based on vCPU and memory resources from container image download to pod termination, billed per second with a one-minute minimum.

To estimate your costs, use the AWS Pricing Calculator.

Maximize your Kubernetes investment with LogicMonitor 

AWS EKS is a system that can streamline and optimize your company. However, many need to be using it to its full potential. Monitoring will help you get the most out of your investment via key metrics and visualizations. 

LogicMonitor offers dedicated Kubernetes monitoring dashboards, including insights into Kubernetes API Server performance, container health, and pod resource usage. These tools provide real-time metrics to help you detect and resolve issues quickly, ensuring a reliable Kubernetes environment. These insights help drive operational efficiency, improve performance, and overcome common Kubernetes challenges.

Learn more here:

• LogicMonitor & AWS: Maximize your Kubernetes Investment with Monitoring
• LogicMonitor’s Kubernetes Monitoring Overview

If you need a cloud monitoring solution, LogicMonitor can help you maximize your investment and modernize your hybrid cloud ecosystem. Sign up for a free trial today!

A growing number of enterprises are shifting toward a multi-cloud environment with the rise of remote and hybrid work. In fact, 76% of organizations have already adapted to a multi-cloud infrastructure.

These dynamic networks offer companies many reported advantages, such as scalability, agility, and optimized performance. When it comes to a company’s digital transformation and transition to a multi-cloud environment, Software-Defined Wide-Area Networking (SD-WAN) often emerges as a top consideration.

What is SD-WAN?

Many companies with a multi-cloud network have replaced the conventional Multiprotocol Label Switching (MPLS) transport protocols with SD-WAN. 

SD-WAN refers to a software-based method of managing wide-area telecommunication networks. With SD-WAN, you can combine transport services (including MPLS circuitry) through encrypted overlay tunnels to communicate and prioritize enterprise data across internal applications. 

There is a good reason for SD-WAN’s widespread appeal. While the MPLS has proven reliable for decades in handling predetermined communication pathways, it lacks the flexibility and agility for managing modern multi-cloud environments with vast and dispersed endpoints. 

Unpacking the SD-WAN architecture

SD-WAN networks run on an abstract infrastructure divided into a control and forwarding plane. The control plane functions from a centralized location as a remotely controlled network, eliminating the need for on-premise technicians. At a granular level, SD-WAN features three components that comprise its virtualized infrastructure, removing the reliance on specific hardware. 

SD-WAN Edge 

The SD-WAN Edge refers to the user endpoint within the network. These may include multi-cloud systems, on-premise data centers, and SaaS platforms. 

SD-WAN Controller

An SD-WAN Controller offers a transparent view of connected networks and facilitates decision-making policies for orchestrators. Essentially, an SD-WAN controller provides centralized management of enterprise data flow and authenticates devices linked to your network. 

SD-WAN Orchestrator

Your designated SD-WAN Orchestrator manages and systematizes policies and traffic among authorized controllers. The component streamlines intuitive workflows across your enterprise networks (e.g., branch offices). Essentially, orchestrators are the definitive bridge between your controller and edge routers. You can upgrade orchestrator functions by providing enhanced analytics and performance SLAs that expedite troubleshooting processes and network fixes.  

Top SD-WAN providers

The modern market features an assortment (and an ever-growing number) of SD-WAN vendors, each providing unique features and functionalities. Therefore, you will benefit from researching the leading vendors to access the best solutions in network function virtualization (NFV) and software-defined networking (SDN) deployments.  

Fortinet Secure SD-WAN

With superior security standards, Fortinet offers services that drive high-performance network capabilities. The vendor’s SD-WAN structure helps your organization manage precious enterprise data without compromising speed or function. Also, Fortinet’s SD-WAN services have undergone rigorous testing, with Gartner validating the solution for its high performance, reliable security, and low total cost of ownership (TCO). 

Using Fortinet’s SD-WAN technology guarantees several improvements to communication processes with built-in encryption protection and sandboxing features that prevent data loss. Fortinet provides frictionless integration to your branch infrastructure for smooth data management across LANs, optimizing hybrid SD-Branch layouts. 

Versa Networks (OS)

Versa Networks’ SD-WAN solution features an integrated platform with premium security capabilities. The technology’s intuitive functions include multi-cloud connectivity, full multi-tenancy and micro-segmentation of businesses, and context-based network and security policies throughout registered networks. 

Versa prioritizes optimal network security as one of its core missions. In 2021, Gartner recognized Versa Networks as a visionary in the Gartner® Magic Quadrant™ for Network Firewalls, emerging as the preferred choice from an in-depth comparison of the top 19 vendors in the communications industry. The SD-WAN offers access to Versa’s Secure Access Service Edge (SASE), enhancing user security through multi-factor authentication, data protection, and SSL decryption.

Aryaka

Aryaka is an innovative service provider that combines SD-WAN technology with a secure web gateway as a one-stop network solution. Specifically, Aryaka’s hybrid approach equips your organization with a zero-trust WAN that significantly reduces business and operational risks. As a result, Aryaka positions itself as a leader among SD-WAN vendors, promoting the fastest service of its kind within the industry. 

Gartner has recognized the zero-trust vendor as the customer’s choice for three consecutive years through outstanding KPI standards, including 99.999% SLA performance and uptime and a 65 net promoter score rating five times the industry average. Your business can manage optimal security and communication performance from a single contact point through Aryaka’s SD-WAN layouts. 

Understanding the pros of SD-WAN

SD-WANs give enterprise networks a general boost from conventional MPLS systems as they improve connectivity across separate applications and off-site locations. 

Business traffic prioritization

SD-WAN helps your organization prioritize critical enterprise data by selecting the most cost-effective and efficient communication path. When you set up the technology’s load-balancing and traffic-steering capabilities, your SD-WAN network can recognize business applications and allocate bandwidth volume according to individual service requirements. Traffic steering lets your team manage multiple parallel connections in business traffic with a responsive system, providing rate-limitless sensitive applications with optimal bandwidth.

Affordability

An SD-WAN approach applies private and distributed data exchange and control measures, which function seamlessly across diverse project environments. The process optimizes network functionality and cost-effectiveness by securing data from the cloud and immediate networks. 

Application performance optimization

SD-WAN’s structured infrastructure drives optimal application performance across enterprise networks. Specifically, the agile transport mode fulfills the latest enterprise compliance mandates and automates traffic steering based on business priorities. Additionally, SD-WAN provides a centralized control center for managing enterprise data across multi-cloud endpoints, connecting with authorized SaaS and IaaS collaborators and vendors without complication. 

Diverse transport methods

With SD-WAN networks, users can access multiple transport channels, including direct broadband connection, 5G, and traditional MPLS circuits. The flexible arrangement improves data availability for undisrupted and optimized communications. You can expect optimal application performance across cloud systems, on-premise servers, and SaaS platforms like Microsoft 365 or Salesforce. 

The cons of SD-WAN

While SD-WAN networks seem like a step in the right direction in multi-cloud environments, they pose some user considerations as a developing technology.  

No on-site security function 

SD-WAN networks lack an on-site security function, so you must separately install and manage a security policy to safeguard networks against online threats. An unprotected SD-WAN infrastructure might face considerable risks from data breaches such as the Colonial Pipeline Hack, which resulted in significant data loss and reputational damage. 

No Quality of Service (QoS) under specific scenarios

Communication networks that rely on SD-WAN provisions lack a proper QoS. Essentially, these networks will not receive the full technical benefits of SD-WAN, including path control, traffic shaping, and forward error correction. 

Vendor concerns

SD-WAN vendors may provide their services and equipment at a higher cost. Also, due to the variability of service standards, some vendors may need more capability to service software-based networking (SDN). 

Revisiting MPLS 

In the 1990s, MPLS replaced standard internet protocol (IP) routing and became the primary transport method for enterprise data. While the infrastructure offers scalability, optimized bandwidth utilization, and enhanced security – by serving as a virtual private network – it requires installing and maintaining physical links. This process has become increasingly complex, costly, and impractical in a progressively multi-cloud landscape.  

MPLS infrastructure

MPLS is a protocol-independent solution with predetermined paths between routers in the MPLS network; each label comprises four components:

• The label value that determines the direction of the data packet
• The traffic class field
• The bottom of the stack flag
• The time-to-life (TTL) field

Functionalities of the MPLS

The MPLS moves network traffic through predetermined labels instead of conventional addresses, guiding the data through private WANs (wide-area networks).

MPLS functions as layer 2.5 in the OSI seven-layer hierarchy between data links that use LANs and networks that run on internet-wide addressing. This infrastructure attributes a forwarding equivalence class (FEC) to each data packet within a network, which routers can decipher by comparing them against descriptive tables.

The routers update the outermost layer of the data packet as it travels through the FEC pathway and to the next hop, which is examined and submitted to the next layer. Users of the MPLS method can customize the information for each packet, essentially driving top performance in unified networks.

Private MPLS networks can provide your organization with a consistent and reliable means of managing communications in cloud-based environments. 

Pros of MPLS

Your MPLS transport modes remain segregated from the public internet, making the infrastructure invulnerable to prevalent web-based attacks such as distributed denial of service (DDoS). As such, the enhanced security of MPLS offers the optimal performance of real-time data transportation by avoiding potential interceptions and packet loss within the open internet.  

Despite the general security of MLPS (with SD-WAN combinations), some decision-makers may seek added protection from automated cloud monitoring across public and private connections. 

Cons of MPLS

Most of the downsides to MPLS relate to its physical limitations and high cost compared to SD-WAN alternatives. In its original design, the MPLS catered to organizations communicating through remote branches of enterprise data centers. MPLS would conventionally backhaul data from branch offices for comprehensive security processing and distribution through on-premise hubs. However, many companies now prefer cloud services over MPLS. Additionally, the backhauling process often increases latency and reduces application performance.

Comparing SD-WAN with MPLS

A significant highlight of SD-WAN, unlike MPLS, lies in its transport-agnostic overlay structure. Your organization can benefit from the arrangement by applying and modifying policies across your WAN from a centralized location. Alternatively, MPLS functions via predetermined routes through physically installed connections, but its fixed circuits make managing changes across multiple user environments costly and complex. 

Although SD-WAN might replace MPLS as the more popular transport choice for some companies, the technologies could co-exist depending on your enterprise arrangements. For instance, some companies may adopt a hybrid network management approach. Specifically, decision-makers would restrict MPLS use to on-premise legacy applications while offloading cloud-based programs to SD-WAN.

Additionally, some organizational leaders have adopted internet-augmented MPLS with SD-WAN. The advanced process increases organizational flexibility by enhancing MPLS with internet broadband links. These links prioritize networking decisions according to specific requirements, such as application type and optimal bandwidth volume. 

Hybrid approaches

Many organizations are adopting hybrid approaches that combine the strengths of SD-WAN and MPLS. This strategy allows businesses to optimize performance and cost-effectiveness by leveraging the unique benefits of each technology for specific use cases.

How hybrid SD-WAN/MPLS solutions work

A hybrid approach integrates MPLS circuits with SD-WAN’s flexible, software-defined overlay. MPLS handles latency-sensitive and mission-critical applications that require guaranteed Quality of Service (QoS), while SD-WAN manages less critical traffic using cost-effective broadband or other transport methods. By dynamically routing traffic based on application requirements, hybrid setups ensure that each data type is delivered efficiently and securely.

For example:

• MPLS role: Ensures low-latency, high-reliability communication for applications like VoIP, video conferencing, and financial transactions.
• SD-WAN role: Routes non-critical traffic, such as email, file backups, and SaaS applications, through broadband connections, reducing MPLS bandwidth requirements and costs.

Scenarios where hybrid approaches excel

1. Real-time applications with high bandwidth demand
   Businesses requiring uninterrupted service for real-time applications, such as hospitals using telemedicine or financial institutions running stock trading platforms, can dedicate MPLS to these tasks while leveraging SD-WAN for less critical operations.
2. Multi-branch organizations
   Enterprises with numerous branch offices can use MPLS for their headquarters and key locations, ensuring consistent performance for sensitive operations while using SD-WAN to connect smaller branches with broadband.
3. Global operations with varying network needs
   Hybrid solutions are ideal for multinational organizations with offices in regions where MPLS availability or affordability varies. In these cases, MPLS can be prioritized in key regions while SD-WAN manages connections in remote or less-developed areas.
4. Disaster recovery and business continuity
   By combining MPLS and SD-WAN, businesses can create highly resilient networks with failover capabilities. If MPLS circuits experience outages, SD-WAN dynamically reroutes traffic to maintain uptime.
5. Cloud-first strategies
   Hybrid approaches enable organizations transitioning to cloud-based operations to retain MPLS for legacy applications while offloading cloud workloads to SD-WAN. This ensures seamless performance across both on-premise and cloud environments.

Decision-making checklist: Choosing between SD-WAN and MPLS

Selecting the right networking solution for your organization requires carefully evaluating your unique needs, priorities, and constraints. Use the following checklist to guide your decision-making process and determine whether SD-WAN, MPLS or a hybrid approach is the best fit for your enterprise:

1. Assess your network requirements

Do you have latency-sensitive applications, such as VoIP, video conferencing, or financial transactions, demanding guaranteed Quality of Service (QoS)?

• If yes, MPLS or a hybrid approach may be necessary.

Are your users distributed across multiple remote locations or regions with varying connectivity needs?

• If yes, SD-WAN offers better scalability and flexibility.

2. Evaluate your budget

What is your budget for networking infrastructure, including installation, maintenance, and operational costs?

• MPLS typically requires higher initial and ongoing investments due to physical circuits and hardware.
• SD-WAN offers a cost-effective alternative by leveraging existing broadband or internet connections.

3. Consider Scalability

Is your organization rapidly expanding or adopting a multi-cloud strategy?

• SD-WAN provides seamless scalability for growing networks and dynamic environments.
• MPLS may need to be more adaptable due to its reliance on fixed circuits.

4. Analyze Security Needs

Do you require private, highly secure connections for sensitive data?

• MPLS offers inherent security through private circuits but may need cloud integration for modern environments.
• For comprehensive protection, SD-WAN requires additional security layers, such as Secure Access Service Edge (SASE).

5. Examine Application Performance

Are your applications cloud-native, such as SaaS platforms or IaaS solutions?

• SD-WAN is optimized for cloud connectivity, enabling direct and efficient access to cloud applications.
• MPLS is more suitable for legacy on-premise applications that rely on data center backhauling.

6. Assess Management and Operational Complexity

Do you need centralized, simplified network management?

• SD-WAN provides centralized control and automation for effortless network monitoring and troubleshooting.
• MPLS requires more hands-on management, often needing on-premise technical support.

7. Plan for Future-Proofing

Is your organization prioritizing digital transformation, including support for hybrid work and zero-trust security models?

• SD-WAN, combined with SASE, aligns with cloud-first and modern security trends.
• MPLS may need help to keep pace with the agility required for these transitions.

8. Evaluate Hybrid Options

Would a combination of SD-WAN and MPLS better meet your needs?

• Use MPLS for critical real-time applications and SD-WAN for cost-effective handling of general traffic.

Alternatives to MPLS and SD-WAN

While MPLS has been a reliable transport method for enterprise networks, advancements in networking technology offer alternative solutions better suited for modern, cloud-first environments. These alternatives provide flexibility, scalability, and cost-efficiency for organizations looking to evolve beyond traditional MPLS setups.

VPN (Virtual Private Network)

VPNs provide a secure, encrypted tunnel for data transmission over the public internet. While they lack the QoS guarantees of MPLS, VPNs are a cost-effective solution for connecting remote users and smaller branch offices to corporate networks. VPNs work well for businesses prioritizing affordability and basic security over high-performance requirements.

5G networks

The rise of 5G technology offers a compelling alternative for enterprise networks. With ultra-low latency, high bandwidth, and widespread availability, 5G networks can support critical business applications that were previously reliant on MPLS. They are particularly effective for edge computing environments and mobile-first businesses.

Internet-based networking

Many organizations are turning to direct internet access (DIA) and broadband connections as replacements for MPLS. These options allow businesses to leverage high-speed, cost-effective public internet connections while pairing them with cloud-native security solutions like SASE to maintain performance and security.

Private LTE and CBRS

Private LTE and Citizen Broadband Radio Service (CBRS) networks are emerging as viable alternatives for enterprises requiring private, secure connectivity without the constraints of traditional MPLS. These technologies enable organizations to create their wireless networks, which are ideal for environments with unique coverage requirements, such as manufacturing facilities or campuses.

A summary of SD-WAN vs. MPLS

SD-WAN systems provide your organization with the trusted capabilities of managing multi-cloud environments with greater scalability and reliability. The modern data transport mode presents a more affordable and flexible solution that leverages MPLS, wireless, broadband, and virtual private networks (VPNs) to maintain high speed across remote environments. 

On the other hand, MPLS boosts network efficiency through predetermined routes, and it is best suited for enterprise environments that continue to rely on data centers. In both instances, you can significantly improve observability by applying a trusted REST API that exposes all functionalities within your networks without tedious wrapper codes. 

REST APIs with multiple integrations offer added convenience for managing data across multi-cloud platforms, preferably with automated webhooks that send real-time information between applications. 

As the WAN continues to evolve, enterprise leaders must have the freedom and accessibility to navigate between private and public Internet infrastructures. Comparing SD-WAN vs. MPLS, you can successfully align your company’s specific requirements with the necessary product sets to achieve the best outcomes.  

SD-WAN in the future of network communications

Through SD-WAN, your organization maintains optimized software functions regardless of location, elevating your overall user experience while reducing IT expenses. Combining SD-WAN networks with intelligent monitoring can help you streamline and optimize business continuity in work-from-home and hybrid settings. 

Another major factor in SD-WAN adoption is its independence from tedious MPLS circuitry migrations. If your enterprise network currently runs on the public internet, you can choose to retain your service provider by moving or reconfiguring the virtualized elements of your WAN.  

Next, SD-WAN capabilities support the core functions of Secure Access Service Edge (SASE) structures, a term Gartner coined in 2019. Advanced SASE setups provide your enterprise with a safe, reliable, unified cloud-based network. 

SASE also helps your organization transport security and access between multiple user endpoints, such as branch offices and mobile applications. The structure operates through a combination of SD-WAN functionalities and cloud-based security solutions. Ultimately, SD-WAN proves integral in supporting your company through future-proofing communications for a cloud-first landscape.

Take your network management to the next level with LogicMonitor. Discover how our platform integrates seamlessly with SD-WAN to provide unparalleled visibility, performance monitoring, and scalability for your enterprise.