Many Spring developers often ask a very reasonable question:

“If I am already using Spring, and Spring provides Micrometer, then why do I even need OpenTelemetry?”

At first glance, this question feels completely valid, because Micrometer is indeed a Spring-supported and well-integrated library, and in many projects it already gives some useful insights. However, to really understand the difference, we need to step back and look at the bigger architectural picture instead of focusing only on one framework or one service.

A Simple Analogy: Thermometer vs Full Medical Diagnosis

Let us start with a simple but powerful analogy.

• Micrometer is like a thermometer.
  A thermometer is a very useful tool. It is simple, easy to use, and gives you a quick signal about whether something might be wrong, for example by telling you that you have a fever. There is absolutely nothing wrong with a thermometer, and in many situations it is exactly what you want.
• OpenTelemetry is like a full medical diagnostic system.
  A complete diagnostic system includes not just a thermometer, but also X-rays, MRI scans, blood tests, and many other lab tests, all working together to give you a much deeper and more reliable understanding of what is actually happening inside the body.

The important point is not that one is “good” and the other is “bad”, but that they operate at very different levels of depth and scope. Comparing Micrometer to OpenTelemetry is a bit like comparing a thermometer to a full hospital diagnostic lab.

Why Micrometer Feels So Attractive in Spring Applications

Micrometer has some very real and practical advantages, especially if you are working in the Spring ecosystem.

• It is deeply integrated into Spring and Spring Boot.
  Because of Spring Boot’s auto-configuration, getting started with Micrometer often feels extremely simple. In many cases, you just add a dependency, change a couple of configuration properties, and you already start seeing metrics.
• It has a very low entry barrier.
  You do not need to understand much about observability architecture or telemetry pipelines to get some value out of Micrometer. This makes it very appealing for small projects or for teams that just want quick visibility into basic metrics.
• For basic metrics use cases, it works quite well.
  If your primary goal is to expose things like CPU usage, memory usage, request counts, or simple timers, Micrometer can do that quite nicely with very little effort.

So yes, Micrometer is a good tool, and there is no need to pretend otherwise.

The Limitations of Micrometer

However, once we move beyond small or medium-sized systems and start thinking in terms of large, distributed microservice architectures, some important limitations become very clear.

• Micrometer is not a complete observability solution.
  Its main focus is on metrics, and while metrics are extremely important, they are only one of the three pillars of observability. Micrometer does not, by itself, give you a complete solution for distributed tracing, which is absolutely critical when you are trying to understand how requests move across many services.
• Distributed tracing is not optional in microservices.
  In a system where a single user request might pass through 10, 20, or even more services, metrics alone can tell you that something is slow, but they cannot easily tell you where exactly the slowness is coming from. Without first-class support for tracing, debugging such systems becomes mostly guesswork.
• Micrometer is tied to the Spring ecosystem.
  This is fine as long as your entire organization lives in the Spring world, but in reality, most large organizations are polyglot, meaning they use Java, Python, Node.js, Go, and other languages side by side.

The Bigger Organizational Problem: Fragmented Observability

Now let us zoom out and look at the problem from an organization-wide perspective.

Imagine an organization with hundreds of microservices:

• Some services are written in Java with Spring.
• Some services are written in Python.
• Some services are written in Node.js.
• Some services might even be written in Go or .NET.

Now imagine that:

• The Java teams say: “We use Spring, so we will use Micrometer.”
• The Python teams say: “We will use some Python-specific library.”
• The Node.js teams say: “We will use something else.”

Very quickly, you end up with:

• Different libraries
• Different data formats
• Different conventions
• Different levels of detail

This leads to inconsistent and fragmented observability across the organization. When a production issue happens and a request fails somewhere deep in the system, you no longer have one coherent, end-to-end view of what happened. Instead, you have to jump between different tools, different dashboards, and different mental models.

This Is Exactly the Problem OpenTelemetry Solves

OpenTelemetry is a standard for observability across languages, frameworks, and teams.

• It provides one common way to produce logs, metrics, and traces.
• It works across Java, Python, Node.js, Go, .NET, and many other ecosystems.
• It allows you to build one consistent observability story for the entire organization, instead of many disconnected ones.

Instead of every team choosing its own tooling and format, the organization can say:

“We use OpenTelemetry everywhere. The backend tools can change, but the instrumentation standard does not.”