Monitoring and O11y: Why You Need Both

Monitoring and O11y Why You Need Both

Developing enterprise IT and software-driven consumer products is becoming more complicated by the day. The growing demand for rapid product upgrades requires streamlined performance and stability. To achieve this stability, companies need effective monitoring and observability practices.

Although monitoring and observability are intertwined, they are not interchangeable. They are designed to operate in a fluid cycle that leads to straightforward visibility. In an ever-evolving technical landscape, combining monitoring and observability efforts is crucial in achieving system visibility – a clear picture of where your system is and where it is supposed to be. This post provides a comprehensive breakdown of both practices, their purposes, their differences, and why combining them is necessary to produce a quality product.

What is monitoring?

Monitoring involves using a monitoring system that looks out for common or known problems, alerting when predetermined thresholds are crossed. Primarily driven by time-series data, good monitoring achieves early warning by proactively identifying trends that might lead to problems. By analyzing and validating the changes in application performance, monitoring plays a significant role in driving product decisions. Monitoring also provides a good view of your system’s health and allows you to see the impact of failures and fixes.

What is observability?

Observability is a practice that focuses on monitoring and analyzing the outputs of applications and the infrastructure that they run on. When systems are identified, observability allows you to ask questions about the data and derive insights into how to improve system performance. And when issues arise, observability enables teams to triage, troubleshoot, and understand the state of the system and the reason behind those issues. Once you’ve identified those previously unknown issues, you can begin monitoring for those things to ensure such problems don’t reoccur. 

The complexity of modern systems requires a way to convert application data into meaningful information, and observability enables developers to derive deeper meaning and smarter solutions from large volumes of data within a short time.

How do observability and monitoring work together?

Monitoring alerts when something is wrong, while observability endeavors to understand why. Although they each serve a different purpose, monitoring and observability significantly complement each other. In many respects, observability is a superset of core monitoring principles and technologies. An observable system is easier to monitor. Monitoring harvests quantitative data from a system through queries, traces, processing events, and errors. Monitoring is querying your system to get answers.

Once the data is available, observability transforms it into insights and analysis, which helps determine the required changes or metrics that need tracking. If you’re not sure what questions to ask when interrogating a system, this is where observability comes into play.

The data from the monitoring stack forms a basis of observability. The observability stack transforms the statistics and analytics into insights that are applied to the monitoring stack to improve the data that it generates. If the system development stack focuses on observability, it allows the developer to answer those questions in an advanced monitoring stack.

Since both processes keep evolving as technology changes, it is crucial to ensure that all the tools, stacks, and platforms related to monitoring and observability are constantly changing. A system’s observability depends on its simplicity and the capability of the monitoring tools to identify the right metrics. Therefore, observability is impossible without some level of monitoring.

How does observability differ from monitoring?

While observability and monitoring may seem similar, they differ in many ways. Understanding where monitoring and observability differ is essential if you don’t want unprecedented change to cause considerable problems in your development life cycle. Here are some of the differences:

Observability is a practice, while monitoring is driven by technology

Monitoring only tells you that a problem happened. Ideally, observability tells you what caused that problem. Monitoring is a process of collecting data from your system, while observability is the system’s ability to be transparent enough to easily track issues. While observability determines the standards a system should meet for analysis, monitoring utilizes those properties to produce the required data and statistics.

Observability is a superset of monitoring and encompasses many other practices

Observability utilizes logs, metrics, and traces, among other data, such as topology and events, to provide enough data from the system to help solve current and potential issues. Monitoring can also be part of the observability process, along with aggregating logs and tracking individual requests. A system can have low or high observability depending on its speed to solve issues. On the other hand, monitoring only collects data and alerts the developer whenever errors occur.

Observability identifies unknowns, while monitoring aims at reporting known errors

This is one of the main ways in which observability differs from monitoring. The core purpose of observability is to handle existing errors and solve incoming issues before users discover them. Monitoring, on the other hand, identifies the problems known by the user.

How does monitoring fit into the larger observability ecosystem?

As enterprises accelerate their initiatives in a software-centric world, staying safe is essential.  Using data from their business outcomes, apps, and the underlying infrastructure, monitoring and observability create a string to pull on when problems arise. That string includes the different types of data previously mentioned – such as metrics, traces, and logs – which are the underpinnings of hybrid observability. Information derived from this data allow you to ask three important questions:

  1. What’s going on in my environment? 
  2. Where is the problem happening? 
  3. Why is that problem happening? 

Answering these questions is key to finding quality solutions as quickly as possible.

What’s going on in my environment? 

By having metrics instrumented all over the environment, customers can clearly see when issues are brewing so they can act on them before they blow up. Metrics mostly appear as time-series data that’s plotted on a graph or chart. Metrics tell you whether you have a problem, but they don’t tell you the root cause. Common examples would be: a CPU at 100%, a disk that’s full, or a network link that’s dropping packets.

Where is the problem happening?

With complex systems having so many moving parts, it’s imperative that you find the right pieces to fix quickly. That’s done via traces. Traces not only provide insight into poorly performing services, they identify interactions between services that are performing poorly, which contributes to poor overall performance or availability. Traces help you identify which kinds of transactions or customers might be affected, and on which systems.

Why is the problem happening?

The final question to ask is why. That’s where logs come in. Logs contain all the unstructured data that reveal exactly what happened, when and why it happened, and the context and details required to build the best solution for that issue.

Answering these three questions – using metrics, traces, and logs that are in alignment with your infrastructure, applications, and business – will enable a dramatically faster resolution.

Does observability encompass monitoring?

It’s important to remember that observability isn’t just monitoring in more complex environments; it’s the fusion of varied types and sources of data to better determine why problems are happening, not simply that a problem is happening. In the past, software developers relied on traditional application monitoring to determine what goes on in a system. Their approach failed to achieve app reliability because of insufficient information from monitoring setups.

However, as systems become more complex and refactored into microservices, the sheer volume of monitoring data becomes impossible to correlate and navigate without help. This led to the adoption of observability practices that no longer limit monitoring to data collection and processing but also make systems observable. The observability platform, also known as the O11y, turns data into useful information by combining streams, performing analysis, and detailing the full life cycle.

A modern log management solution requires a platform that supports your stack of infrastructure, applications, and services. Rather than spend long hours pulling out many codes, analyzing errors, and generating messages without context, an observability platform relieves developers by improving their ability to ask the system questions and find answers. The result is better application performance, minimized downtime, and enhanced user satisfaction.

When designing a dependable system, there is a need for collaboration among cross-functional Devs, ITOps, and QA personnel. High demands from both the internal business units and the ultimate end users make it vital for large and small companies to identify and respond to errors fast and in real-time. Traditional monitoring that relies on error logs only scratches the surface. Observability goes beyond collecting error data and can now analyze that data, identify trends, gain insights, and understand the overall health of the system.

Observability can help developers:

  • Get more insights into the apps they develop
  • Automate testing
  • Release better quality code, faster

With observability, productivity improves. Now teams are freed up to optimize collaboration, build better relationships, and focus on innovative solutions and digital transformation. Better still, the end user benefits from a high-quality and refined user experience.

What makes monitoring stand on its own?

Monitoring plays a crucial role in building dashboards, alerting, and analyzing long-term trends. However, it is difficult to make predictions when monitoring complex distributed apps because production failures are not linear. Despite these limitations, monitoring remains a crucial tool for developing and operating microservice-based systems.

Monitoring metrics need to be straightforward and focused on actionable data to get a good view of the system’s state. Since it’s an excellent mechanism to know the current and historical state of a distributed system, monitoring should be considered in every stage of the software development life cycle. The monitoring data helps developers find issues, root-causing failures, debuggability, patterns analysis, and many more features useful for all teams. Monitoring provides the information, statistics, and alerts to trigger actions that will make those events visible to the interested parties.

What are the benefits of monitoring and observability?

Adopting both observability and monitoring benefits the developers and end users alike.


Here are just a few ways that monitoring solutions serve the business:

Save costs

Monitoring tools provide real-time alerts whenever issues arise in the system. This means that you can save money and avoid costly losses by resolving the problems as soon as they occur. Lack of a monitoring setup means organizations waste more resources and money in troubleshooting.

Reduce risk

Monitoring reduces the risk of system intrusions. Any attack attempts or suspicious activities are easy to detect because the system sends alerts to the monitoring teams. Then, teams can respond fast, neutralize the risk, and keep the system secure.

Increase productivity

Every organization wants to increase operational efficiency and productivity. Reinforcing DevOps teams with real-time insights and alerts makes it easy to isolate incident causes and fix them within the shortest time possible. With reduced downtime, teams are more productive and the end-user experience is better. Furthermore, the ability to detect underlying issues before they cause rippling problems can mitigate downtime. All of this allows teams to focus on more strategic initiatives and get the job done efficiently.

Enhance flexibility

Monitoring solutions are highly flexible. Unlike observability, most monitoring solutions are not embedded inside applications’ data source code. Therefore it is easy to switch between different solutions.


Observability is more about the relationship between data than the data itself. The goal is to focus more on outcomes and make sure that the same problems don’t occur over and over again. A proper observability setup delivers powerful benefits to IT teams, organizations, and end users alike. Some of them include:

Eliminate debugging

The observability platform offers constant surveillance to production applications, making it easier to track issues and ensure that collected and analyzed data can be correlated and used to preempt issues even before they arise.

Monitor health

Rather than merely logging numbers, observability helps determine the app’s health by providing insights into the applications’ future performance. Now you can develop an application while considering the changes required to improve overall health and performance and the metrics required to be tracked.

Build better apps

Observability is a fundamental property of an application and its supporting infrastructure. Since developers create an application to be observable, the DevSecOps teams can easily interpret the observable data during the software delivery life cycle to build better, more secure, more resilient applications.

Automate everything

Observability supports intelligent systems that can self-heal and recover from relatively minor issues without the need for human intervention. An advanced observability solution can also automate more processes, increasing efficiency and innovation among Ops and Apps teams.

Improve customer experience

A good user experience boosts the company’s reputation, grows revenue, and gives the company a high competitive edge by increasing customer satisfaction and retention.

Increase productivity

The infrastructure teams can leverage observability solutions to reduce the time to identify and resolve issues, improve application uptime and performance, and optimize cloud resource utilization.

Getting started with observability and monitoring

As IT environments become more complicated, achieving hybrid observability doesn’t have to be. Managing distributed system infrastructures requires an equally effective set of tools to monitor, analyze, and trace events. These tools allow developers to understand system behaviors and prevent future system problems. The following steps are crucial in implementing observability:

Choose a centralized observability platform

A good monitoring solution is the bedrock of an excellent observability setup. Developers, site reliability teams, and project managers must look for a monitoring solution that suits their needs and unifies data from all the telemetry into one location, explored through a single interface. The solution should be flexible enough to factor in the requirements’ growth as the platform keeps evolving. When done well, developers and operations teams can turn off point tools that have been creating data sprawl and excessive administration effort.

Analyze the metrics of your application

In this step, teams need to analyze the metrics carefully, considering all the metrics of their applications. Missed metrics can make the troubleshooting time-consuming because they might represent an inaccurate trend. Choosing metrics carefully also eliminates redundant data, which might not provide helpful insights and analysis. The monitoring setup is as efficient as the speed at which you can access the log information about errors.

After setting up a centralized monitoring system and receiving useful logs, it is essential to determine what to do with the data. This can be done through machine learning, where algorithms convert the accumulated data into valuable insights, allowing you to do more in less time. Machine learning helps analyze what is going on in a system and raise an alert before the issues tamper.

The bottom line

Although observability and monitoring work in tandem, it is essential to know that they differ. Observability is a practice that includes monitoring. The choice of either depends on the size of your application, reliability, and goals. Either way, both solutions are necessary to keep crucial systems and applications up and available, enabling a business to innovate and move forward.