OpenTelemetry is a set of tools that help software developers see what their applications are doing while they run

When you use an app or website, a lot happens behind the scenes that you never see. Data moves between servers, databases store information, and different pieces of software talk to each other. If something breaks or runs slowly, the people who built it need to know where the problem happened and why. OpenTelemetry is the standard way they collect that information.

Think of it like this: if your car's engine starts making a noise, a mechanic plugs in a diagnostic tool to see what the engine is actually doing — temperature, fuel pressure, timing. OpenTelemetry does the same thing for software. It watches applications while they work and records what happened, how long it took, and whether anything went wrong.

Key Takeaways

  • OpenTelemetry collects three types of data from running software: logs (written records of events), metrics (measurements like speed or errors), and traces (the path a single request takes through a system).
  • It is a free, open-source standard that works across different programming languages and cloud providers, so companies do not have to rebuild their monitoring tools when they switch technology.
  • OpenTelemetry itself does not store or display data — it only collects it and sends it to separate tools like Datadog, New Relic, or Prometheus that actually analyze and show it to engineers.
  • Companies use it because it reduces the cost of monitoring and makes it easier to move applications between different cloud services without losing visibility into how they work.

The three types of data OpenTelemetry collects

Logs are the simplest: they are written records of what happened. When a user logs into an app, when a payment processes, when an error occurs — the software writes it down with a timestamp. Logs answer the question "what events happened and when?"

Metrics are measurements over time. How many requests did the server handle per second? How much memory is the process using? What percentage of requests failed? Metrics answer the question "what is the overall health and performance right now?"

Traces follow a single request from start to finish. When you click "buy now" on a shopping site, your request might travel through five different services: the website itself, a payment processor, an inventory system, a shipping calculator, and a database. A trace shows the entire path, how long each step took, and where it slowed down or failed. Traces answer the question "why did this one request take so long or break?"

Why companies switched to OpenTelemetry

Before OpenTelemetry became standard, each company built its own way to monitor applications. If you used Amazon Web Services, you might use CloudWatch. If you switched to Google Cloud, you would use Stackdriver. If you moved to a different monitoring company like Datadog, you had to rewrite all your monitoring code.

OpenTelemetry solved this by creating one standard language that works everywhere. A developer writes the monitoring code once, and it works whether the process runs on Amazon, Google, Microsoft Azure, or a company's own servers. When a company switches cloud providers or monitoring tools, they do not have to rewrite anything — they just point OpenTelemetry at the new destination.

This also made monitoring cheaper. Before, companies paid for expensive proprietary monitoring tools. Now they can use free, open-source tools built on OpenTelemetry and only pay for storage and analysis if they want advanced features.

How OpenTelemetry actually works in practice

A developer adds OpenTelemetry libraries to their code — these are small packages written in the same programming language as the process. When the process runs, these libraries automatically record logs, metrics, and traces without the developer having to write custom code for each one.

OpenTelemetry then sends all that data to a collector — a separate piece of software that receives the raw data, can filter or process it, and forwards it to wherever the company wants it stored. The collector might send logs to one place, metrics to another, and traces to a third, depending on what the company uses.

The actual storage and display happens in separate tools. Prometheus stores metrics and creates graphs. Jaeger displays traces visually. Elasticsearch stores logs and makes them searchable. OpenTelemetry does not do any of this itself — it only collects and ships the data. This separation means companies can swap out any piece without affecting the others.

What OpenTelemetry does not do

OpenTelemetry is not a complete monitoring solution by itself. It does not have a dashboard where you can see pretty graphs. It does not alert you when something breaks. It does not store data long-term. It is purely a collection and transport layer — the pipes that carry information from the process to the tools that actually use it.

It also does not monitor everything automatically. Developers have to decide what to measure and add the right OpenTelemetry code to capture it. A poorly instrumented process will send incomplete data, and engineers will miss problems.

Who uses OpenTelemetry and why it matters to you

Large tech companies, financial institutions, and cloud providers use OpenTelemetry because they run complex systems with hundreds of services talking to each other. When something breaks, they need to find the problem in seconds, not hours. OpenTelemetry makes that possible.

For you as a user, it means the apps and websites you rely on are more likely to stay up and run fast. When engineers can see exactly what is happening inside their systems, they catch problems before they affect you, fix them faster when they do happen, and make better decisions about where to invest in improvements.

OpenTelemetry versus other monitoring approaches

Some older applications use process Performance Monitoring (APM) tools that are built by a single vendor — New Relic, Datadog, or Elastic. These tools work well but lock you into one company. If you want to switch, you have to rewrite your monitoring code.

Other companies build custom monitoring from scratch, writing their own code to collect logs and metrics. This gives them complete control but costs a lot in engineering time and is hard to maintain.

OpenTelemetry sits in the middle: it is standardized and free, so you are not locked in, but it still requires developers to write code to use it. It has become the industry standard because it balances flexibility, cost, and the ability to move between tools and cloud providers without starting over.

Frequently Asked Questions

Is OpenTelemetry the same as a monitoring tool?

No. OpenTelemetry collects data from applications. Monitoring tools like Datadog or Prometheus receive that data and display it, alert on it, and store it. OpenTelemetry is the collection layer; the monitoring tool is what you actually look at.

Do I need to know about OpenTelemetry to use an app or website?

No. OpenTelemetry is only relevant to the engineers who build and maintain software. As a user, you benefit from it indirectly because it helps them keep services running smoothly, but you never interact with it directly.

Does OpenTelemetry track personal information about me?

OpenTelemetry itself is just a tool for collecting data. What data gets collected depends on how developers use it. Responsible companies configure it to collect only technical information about how the system works, not personal details. You can check a service's privacy policy to see what they monitor.

Can I use OpenTelemetry with any programming language?

Yes. OpenTelemetry has libraries for Python, Java, Go, JavaScript, .NET, Ruby, and many others. This is one reason it became the standard — developers can use it no matter what language they write in.

What happens to the data OpenTelemetry collects?

OpenTelemetry sends it to a collector, which forwards it to wherever the company configured it to go — usually a storage system and analysis tool. The company decides how long to keep it, who can see it, and whether to delete it. This is why privacy policies matter: they should tell you what happens to monitoring data.