eBook
Take control of your VMware-to-AWS migration with an observability-first approach that delivers clarity, continuity, and cost-efficiency from the start.
If you’re planning or evaluating a move to Amazon Web Services (AWS), this ebook is your guide to executing a smarter, more controlled migration. Explore a proven, three-phase approach to migrating from VMware to AWS, designed to reduce risk, maintain visibility, and optimize costs at every stage.
Phase 1: Pre-Migration Planning
Gain a complete view of your current environment with automated discovery, dependency mapping, and capacity planning powered by LogicMonitor.
Phase 2: Migration Execution
Ensure service continuity with real-time telemetry, intelligent alerting, and root cause analysis throughout the transition.
Phase 3: Post-Migration Optimization
Validate performance, manage cloud spend, and prepare for scale with consolidated cost views, optimization recommendations, and forecast modeling.
You’ll also get a practical checklist for next steps, including risk and readiness assessments, migration strategy guidance, AWS incentive opportunities, and how to maintain visibility after cutover.
Solution Brief
LogicMonitor’s LM Cloud, part of the AI-powered LM Envision platform, offers real-time insights across hybrid cloud deployments, enabling Platform Engineering and CloudOps teams to reduce migration risks, manage costs, and swiftly address issues.
Customers like Hain Celestial Group, John Holland, and True Fit have experienced LogicMonitor’s transformative impact, from rapid problem identification to enhanced resource optimization. With LogicMonitor, organizations gain a powerful ally in optimizing their IT environments, ensuring high service availability, and proactively detecting issues before they impact performance.
Elevate your hybrid cloud monitoring with LogicMonitor and drive your business forward with confidence.
On-demand webinar:
Navigating the complexities of hybrid cloud environments? Join us for our exclusive webinar. This event is an unmissable opportunity for IT professionals looking to enhance their cloud strategies with advanced AI-driven solutions.
As hybrid cloud infrastructures become increasingly prevalent, the challenges of managing and monitoring these environments grow. Our webinar addresses these issues head-on, offering insights into innovative monitoring solutions that bring clarity and efficiency to your cloud operations.
We’ll explore how LogicMonitor, in collaboration with AWS, provides unparalleled visibility into both AWS and on-premises deployments, leveraging the latest in cloud monitoring technology.
What you’ll learn:
Discover how to navigate the cloud landscape with confidence and efficiency. Embrace the future of hybrid cloud monitoring with LogicMonitor and AWS.
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:
Let’s start by understanding what each “Ops” term means on its own.
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.
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.
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:
| Term | Focus Area | Primary Users | Core Purpose |
| DevOps | Application delivery automation | Developers, DevOps teams | Automate and accelerate code releases |
| MLOps | Machine learning lifecycle management | ML engineers, data scientists | Deploy, monitor, and retrain ML models |
| AIOps | IT operations and incident intelligence | IT Ops teams, SREs | Reduce alert fatigue, detect anomalies, predict outages |
| Agentic AIOps | Autonomous incident response | IT Ops, platform teams | Automate real-time resolution with AI agents |
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.
DevOps automates the build-and-release cycle. It reduces errors, accelerates deployments, and helps teams ship with greater confidence and consistency.
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.
MLOps ensures machine learning models stay accurate, stable, and useful over time.
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.
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.
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.
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.
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.
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.
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 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 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.
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.
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.
This is the fourth blog in our Azure Monitoring series, and this time, we’re digging into cost efficiency. Azure makes it easy to scale, but just as easy to overspend. Idle VMs, forgotten disks, and silent data transfer fees add up fast. The result is budget overruns that catch teams off guard and force reactive cuts. This blog breaks down the Azure metrics that actually help you reduce waste, improve visibility, and keep cloud spend aligned with business priorities. Missed our earlier posts? Catch up.
Cloud costs scale fast. Compute, storage, networking, and API calls all add up, and without the right metrics, it’s easy to lose track of where the money is going. Many teams monitor overall spend but miss the operational inefficiencies that drive waste.
The key to cost efficiency is using data to pinpoint unnecessary expenses while keeping performance strong. In 2024, 80% of organizations overshot their cloud budgets by 20–50% without effective cost controls. Tracking the right cost metrics makes the difference between staying in control and scrambling to justify unexpected bills.
Cloud costs add up fast, and it’s usually not because of one big mistake. It’s the slow creep of underused resources, oversized instances, and things no one remembers setting up. That’s why it pays to keep a close eye on how your infrastructure is actually being used. When you understand what’s pulling weight and what’s just pulling budget, it’s a lot easier to make smart decisions and stay ahead of surprises.
It’s one of the most common and most expensive cloud mistakes: overprovisioning VMs “just in case.” The result is just massive waste. When teams don’t track how VMs are actually used, they end up paying for headroom they don’t need. That’s why right-sizing starts with metrics.
Metrics to track:
Platforms like LogicMonitor Envision make this easier by automatically surfacing patterns. You can group utilization by instance type, workload, or environment, so instead of scanning thousands of VMs, you can take action where it counts.
Storage costs often climb quietly, without any alerts or obvious symptoms. Teams hold onto old backups, store infrequently accessed data in premium tiers, and leave unattached disks behind after deleting VMs. These are silent budget killers hiding in plain sight.
Key metrics to track:
Azure bills for all managed disks, whether they’re attached to a VM or not. Monthly audits catch this kind of waste early and consistently. Platforms like LM Envision make it easier to surface these patterns at scale, allowing teams to free up capacity and reduce spending without compromising reliability.
As environments scale, temporary resources have a habit of becoming permanent line items. Engineers spin up environments for testing or troubleshooting and forget to shut them down. Services get deprecated, but the infrastructure they relied on continues to run and generate billing.
Key signals of waste:
Cloud budgets break down when teams can’t see who’s spending what. Without clear attribution, costs get centralized, ownership gets blurred, and optimization becomes someone else’s problem.
The fix starts with tagging and grouping resources by team, environment, or application. That turns vague spend reports into actionable insights.
Key focus areas:
Tagging policies need to be enforced. When done right, platforms like LM Envision can automatically read and surface those tags, making it easy to group resources, build cost dashboards, and tie usage back to ownership without manual sorting.
Cloud budgets can quickly spiral out of control when spending isn’t actively monitored. Setting up tracking mechanisms helps teams stay ahead of unexpected costs and maintain financial discipline.
Most budget overruns don’t come from massive events. They creep up over time. Without real-time tracking, teams find out they’ve blown past budget only after the invoice hits.
Staying in control means monitoring actual spend against expectations throughout the month, and not just at the end of the month.
Key metrics to watch:
Visibility drives accountability. With LM Envision, teams can build live budget dashboards using Azure’s billing APIs, helping make sure that finance and engineering stay on the same page.
Tracking cost data is only the start. To see real savings, teams need to take action and continuously fine-tune their environments.
Not all workloads require continuous operation 24/7. Scheduling non-production resources during business hours can dramatically reduce waste.
Track:
Automation is key. Manual shutdowns don’t scale, so make sure to enforce schedules with tools that do the work for you.
Some Azure services rack up charges that aren’t immediately obvious. Without careful tracking, these hidden costs can quickly add up and lead to budget overruns. Some metrics to watch are:
Misconfigurations, runaway queries, and scaling glitches are common and expensive. Without proactive alerts, cost anomalies go unnoticed until the damage is done.
Track for:
Tooling helps. Culture sustains.
Financial efficiency happens when teams own their usage, see their impact, and have the data to make smarter decisions every sprint, not just during budget reviews.
Build the habit:
The teams that stay ahead of Azure spend treat cost metrics the same way they treat performance metrics: constantly tuned, tied to outcomes, and backed by automation.
Cost optimization isn’t a one-time project. The best teams make financial efficiency an ongoing part of their cloud operations. With the right metrics and a unified observability platform—a level up from traditional monitoring—teams can identify cost drivers early, align infrastructure to real demand, and shift cost control from reactive to routine.
Next, we’ll shift our focus from cost to risk, breaking down the metrics that matter for security, availability, and compliance. From suspicious authentication patterns to resource health and service-level reliability, we’ll show you how to stay resilient and audit-ready.
whitepaper
Comprehensive AWS monitoring and cost optimization are critical for today’s cloud environments.
Our white paper, Monitoring AWS Beyond CloudWatch, is designed for CloudOps and IT leaders managing complex AWS infrastructures. Inside, you’ll discover how comprehensive observability helps your team:
And as a technical leader, you’ll take away:
Master full-stack observability and maximize your AWS investment.
on-demand webinar
Whether you’re moving from data centers or public cloud VMs to Amazon Web Services (AWS), every migration faces risks and challenges. See how LogicMonitor’s hybrid cloud observability capabilities help you spot issues and maintain performance throughout your AWS migration.
LogicMonitor’s Cloud Observability Product Marketing Lead, Ismath Mohideen, is joined by Sr. Sales Engineer, Nicholas Doane, to walk through the key stages of a cloud migration journey—sharing best practices and showcasing how LogicMonitor supports enterprises throughout every step.
We’ll share a demo of how LogicMonitor’s AI-powered observability platform helps you solve these migration challenges.
Regardless of where you’re at in your AWS migration journey, discover how to approach hybrid visibility for successful workload and application transitions.
Whitepaper
Challenges like monitoring data across environments, engaging teammates across IT and cloud teams, creating visibility, and reducing blind spots.
Add to that complex cloud costs, and it’s easy to see why the process can be daunting.
This roadmap helps organizations develop a successful strategy for their unique cloud migration journey regardless of their current infrastructure.
Navigating the complexities of hybrid cloud environments? Join us for our exclusive webinar. This event is an unmissable opportunity for IT professionals looking to enhance their cloud strategies with advanced AI-driven solutions.
As hybrid cloud infrastructures become increasingly prevalent, the challenges of managing and monitoring these environments grow. Our webinar addresses these issues head-on, offering insights into innovative monitoring solutions that bring clarity and efficiency to your cloud operations.
We’ll explore how LogicMonitor, in collaboration with AWS, provides unparalleled visibility into both AWS and on-premises deployments, leveraging the latest in cloud monitoring technology.
What you’ll learn:
Discover how to navigate the cloud landscape with confidence and efficiency. Embrace the future of hybrid cloud monitoring with LogicMonitor and AWS.
Watch the webinar below.
As hybrid cloud infrastructures become increasingly prevalent, the challenges of managing and monitoring these environments grow.
Our webinar addresses these issues head-on, offering insights into innovative monitoring solutions that bring clarity and efficiency to your cloud operations. We’ll explore how LogicMonitor, in collaboration with AWS, provides unparalleled visibility into both AWS and on-premises deployments, leveraging the latest in cloud monitoring technology.
What you’ll learn:
