OpenTelemetry is a set of tools that help software teams see what their applications are actually doing

When you run software — whether it's a website, a mobile app, or a service running on a company's servers — you need to know if it's working correctly. Is it fast? Is it crashing? Where is it spending its time? OpenTelemetry is a free, open-source project that collects this information automatically and sends it to monitoring tools so engineers can answer those questions.

Think of it like adding sensors throughout a building. Instead of guessing why a room feels cold, you install thermometers in different places and read the data. OpenTelemetry installs "sensors" in code to measure response times, track errors, count how many requests arrive, and record what's happening at each step. The data flows to monitoring platforms — tools like Datadog, New Relic, or Grafana — where teams can see patterns and spot problems before users notice them.

The project is maintained by the Cloud Native Computing Foundation, the same organization behind Kubernetes. It's become the standard way modern software teams observe their applications, which is why you'll hear the term "observability" used alongside it.

Key Takeaways

  • OpenTelemetry collects three types of data from running software: traces (the path a request takes), metrics (counts and measurements), and logs (timestamped events).
  • It works by adding lightweight code to your application that records what's happening, then sends that data to a monitoring platform of your choice.
  • The main benefit is that teams can see problems in production without having to guess or dig through code — they can watch requests flow through their system in real time.
  • OpenTelemetry is vendor-neutral, meaning you can switch monitoring tools without rewriting the code that collects the data.

The three types of data OpenTelemetry collects

Traces follow a single request as it moves through your system. Imagine a customer placing an order on a website. A trace would record: the request arrives at the web server (5ms), the server queries the database (120ms), the database returns data (2ms), the server processes the response (8ms), and the response goes back to the customer (3ms). If something is slow, the trace shows exactly where the delay happens.

Metrics are counts and measurements over time. How many requests arrived in the last minute? What's the average response time? How much memory is the application using? Metrics let you spot trends — if response time is creeping up every day, something is getting worse even if it hasn't broken yet.

Logs are timestamped messages that applications write out. "User logged in at 2:45 PM," "Database connection failed," "Cache was cleared." Logs are the oldest form of observability, but OpenTelemetry ties them together with traces and metrics so you can see the full picture — not just that something failed, but what requests were affected and how many.

How OpenTelemetry actually works in code

A developer adds OpenTelemetry libraries to their application. These libraries are available for most programming languages: Python, Java, Go, Node.js, C#, and others. The libraries sit in the background and automatically instrument the code — they hook into common operations like database calls, HTTP requests, and file reads.

When the application runs, the instrumentation records what's happening. A request comes in, the code makes a database query, the query returns — all of this is captured. The OpenTelemetry libraries then send this data to an exporter, which is a piece of code that formats the data and sends it somewhere. That "somewhere" is usually a monitoring platform, but it could also be a local file or a custom system.

The beauty of this design is separation: the code that collects data (OpenTelemetry) is separate from the code that stores and displays it (the monitoring platform). A team can switch from one monitoring tool to another without touching the application code — they just change the exporter configuration.

Why teams choose OpenTelemetry over building their own monitoring

Before OpenTelemetry became standard, teams either built custom monitoring code or used proprietary tools that locked them into one vendor. Custom code meant writing and maintaining thousands of lines of instrumentation. Proprietary tools meant that switching platforms was expensive and difficult.

OpenTelemetry solves both problems. It's free, it's maintained by a large community, and it works the same way across different programming languages and frameworks. A Python team and a Java team in the same company can both use OpenTelemetry and send data to the same monitoring platform, using the same concepts and the same data format.

It also handles the hard parts automatically. Distributed tracing — following a request across multiple services — is notoriously difficult to build from scratch. OpenTelemetry handles it. Sampling — deciding which data to keep when there's too much to store — is built in. Context propagation — making sure related events stay connected — is handled automatically.

The difference between OpenTelemetry and monitoring platforms

OpenTelemetry collects and exports data. It does not store it, display it, or alert you when something goes wrong. That's the job of a monitoring platform. OpenTelemetry is like a camera that records video; the monitoring platform is the system that stores the video, lets you search it, and alerts you if something unusual appears.

Popular monitoring platforms that work with OpenTelemetry include Datadog, New Relic, Grafana Cloud, Honeycomb, and Lightstep. Some are commercial, some are open-source. Some are designed for large enterprises, others for smaller teams. OpenTelemetry doesn't care — it sends the same data to all of them.

This is why OpenTelemetry matters: it means you're not locked into one vendor's way of collecting data. You can start with one platform, switch to another, or even send data to multiple platforms at once. The instrumentation stays the same.

What OpenTelemetry does not do

OpenTelemetry does not monitor your infrastructure — that's the job of tools like Prometheus or Grafana Agent, which track CPU, memory, disk, and network at the system level. OpenTelemetry focuses on application-level observability: what your code is doing, not what the server it's running on is doing.

It also does not provide security monitoring or log aggregation in the traditional sense. It can send logs to a monitoring platform, but if you need to search through millions of log lines for compliance reasons, you'd typically use a dedicated log management tool like Splunk or ELK Stack.

And it does not automatically fix problems. It tells you what's happening so you can decide what to do. A trace might show that a database query is taking 5 seconds, but OpenTelemetry won't optimize the query for you — that's the engineer's job.

Getting started with OpenTelemetry

If you're a developer, the first step is choosing a monitoring platform. Most platforms have documentation on how to set up OpenTelemetry. You'll add the OpenTelemetry libraries to your project using your language's package manager — pip for Python, npm for Node.js, Maven for Java, and so on.

Then you configure the exporter to point to your monitoring platform. This is usually a few lines of configuration: the platform's URL, an API key, and which data you want to send. Many platforms offer automatic instrumentation, which means you don't have to add code to every function — the libraries do it for you.

If you're not a developer but work with software teams, understanding OpenTelemetry helps you understand what data is available and what questions you can ask. Instead of "the system is slow," you can ask "which service is slow, and at what step?" The data is there; OpenTelemetry just makes it easier to collect and use.

Frequently Asked Questions

Does OpenTelemetry slow down my application?

Instrumentation adds a small overhead, usually less than 5% in CPU and memory. Most of the cost comes from sending data over the network, which is why sampling is important — you don't have to keep every trace, just a representative sample. Teams typically keep 1 to 10% of traces in production and 100% in development.

Can I use OpenTelemetry with legacy applications?

Yes, but it requires adding the libraries and configuration to your code. You can't observe an application you can't modify. Some monitoring platforms offer agent-based instrumentation that doesn't require code changes, but that's not OpenTelemetry itself — it's a separate tool that works alongside it.

What's the difference between OpenTelemetry and APM tools?

APM (Application Performance Monitoring) tools like New Relic and Datadog are monitoring platforms. OpenTelemetry is the standard way to collect data that those platforms use. Some APM tools have their own proprietary instrumentation, but most now support OpenTelemetry because it's becoming the industry standard.

Is OpenTelemetry only for cloud applications?

No. OpenTelemetry works for any application — cloud, on-premises, mobile, desktop. It's most common in cloud and microservices environments because those systems are harder to debug, but the principles apply everywhere.

Do I have to use OpenTelemetry if I'm already using a monitoring tool?

Not necessarily. If your current tool works and you're not planning to switch, you can keep using its proprietary instrumentation. But if you want flexibility, or if you're building new applications, OpenTelemetry is the safer choice because it doesn't lock you into one vendor.