When we build modern applications, especially applications based on microservices and distributed architectures, one of the hardest problems is not just writing code, but understanding what is actually happening inside the system when something goes wrong in production. Very often, teams have dashboards and alerts, yet they still struggle to explain why the system is slow or why certain requests are failing. This is exactly where the concepts of monitoring and observability become extremely important.

Although these two terms are frequently used as if they mean the same thing, they actually represent two different levels of understanding about a system. By clearly understanding the difference between them, you will also understand why modern systems require observability in addition to traditional monitoring.

A Simple Real-World Analogy: The Human Body

Let us start with an analogy that almost everyone can relate to: the human body.

• Under normal conditions, the human body maintains a temperature roughly between 97°F and 99°F, and as long as the temperature stays in this range, we generally consider ourselves healthy and do not worry about our condition. This is similar to an application operating within its normal performance limits, where no alerts are raised and everything appears fine from the outside.
• When the temperature rises above 100.4°F, we say that we have a fever, and at that moment we immediately know that something is wrong with the body. However, it is important to realize that fever itself is not the real problem, but only a signal or symptom that something deeper is wrong inside the body.
• When we visit a doctor, the doctor does not treat the fever blindly, but instead orders tests such as a blood test or a complete blood count (CBC). By looking at these reports, the doctor might find, for example, that the white blood cell count is high, which indicates a bacterial infection, and only then does the doctor prescribe an antibiotic that actually fixes the root cause of the problem.

This analogy maps almost perfectly to how monitoring and observability work in software systems.

Monitoring: Knowing That Something Is Wrong

In software systems, monitoring plays the same role as checking body temperature.

• Monitoring focuses on predefined metrics such as CPU usage, memory usage, response time, and error rate, and it continuously checks whether these values are within acceptable limits. When one of these metrics crosses a threshold, the monitoring system raises an alert and tells the team that something is wrong with the application.
• The most important limitation of monitoring is that it treats the application as a black box. It can tell you that the application is slow or unhealthy, but it cannot explain which internal component is responsible or why the problem is happening.
• In other words, monitoring is excellent at detecting symptoms, but it is not sufficient for finding the root cause of complex problems, especially in distributed systems.

Just like fever tells you that something is wrong with the body, monitoring tells you that something is wrong with the application.

Observability: Understanding Why It Is Wrong

Observability goes one step deeper and plays the same role as the medical test reports in our analogy.

• Observability is about understanding the internal state of the system by analyzing the data it produces, such as logs, metrics, and traces. Instead of just looking at the final outcome, you can see how a request flows through different services and where time is being spent.
• In a microservices architecture, a single user request may pass through many services and databases, and with observability, you can see the complete journey of that request. This makes it possible to identify exactly which service or which dependency is the real bottleneck.
• Once you know the real cause, the fix becomes much more precise and effective, because you are no longer guessing. For example, if you see that most of the time is being spent in one slow database query, you can add an index or optimize that query instead of making random changes elsewhere.

In simple terms, observability turns your system from a black box into a glass box.

A Practical Example: Slow Application in Production

Let us consider a realistic scenario.

• Suppose your team has decided that 800 milliseconds is a good response time for an API, and anything close to this value is considered healthy. However, if the response time goes beyond 2 seconds, you treat it as a serious performance problem.
• One day, users start complaining that the application is very slow, and at the same time your monitoring system starts raising alerts saying that the response time has crossed 2 seconds. At this point, you clearly know that there is a problem, but you still do not know why the problem is happening.
• If your system has good observability in place, you can look at a distributed trace of a slow request and immediately see that, for example, one particular service is spending most of the time waiting for a slow database query. This single observation already explains the entire problem.

Now you are in a position to apply a targeted fix, such as adding an index or optimizing that query, instead of trying random performance tweaks across the system.

Formal Definitions

Monitoring

• Monitoring is the process of collecting and analyzing predefined metrics such as CPU usage, memory usage, response time, and error rate in order to detect problems and raise alerts when the system crosses certain thresholds.
• It is extremely useful for knowing when something is wrong, but by design it does not tell you much about the internal behavior of the system.

Observability

• Observability is the ability to understand the internal state of a system by analyzing the outputs it produces, especially logs, metrics, and traces.
• It allows you not only to detect that a problem exists, but also to explain exactly why it exists.

Black Box vs Glass Box Thinking

• With monitoring, the system is mostly a black box, because you only see high-level signals and symptoms, but you cannot easily explain what is happening inside.
• With observability, the system becomes a glass box, because you can see how requests flow through the system and where time and resources are actually being consumed.

Why This Matters in Microservices

• In a monolithic application, it is sometimes possible to debug issues using just logs and a debugger, because everything runs in one place and the execution flow is relatively easy to follow.
• In a microservices architecture, however, a single request may involve many services and external systems, and without observability, you will almost always struggle to find the real root cause of performance and reliability problems.