OpenTelemetry & OpenMetrics

To build great applications, we must understand how our systems work. We need to see what happens inside our application as it runs. This observability allows us to debug issues before they happen, identify bottlenecks, and make changes to help our system run faster and more efficiently.

Most developers choose between two popular open source solutions: OpenTelemetry and OpenMetrics. Which should you use?

Either option is useful, but both come with their own set of pros and cons. For example, OpenTelemetry provides higher-level observability features such as tracing and profiling, while OpenMetrics focuses on lower-level metrics such as memory usage.

We’ll review the differences between the two projects and explain why you should consider using one or the other.

For your convenience, here is a summary of the topics we’ll be discussing below:

  • OpenTelemetry: A Unified Standard for Observability
    • How OpenTelemetry Works
    • OpenTelemetry’s Advantages
    • Is OpenTelemetry Free?
  • OpenMetrics: A Standard for Transmitting Cloud-Native Metrics
    • How OpenMetrics Works
    • Where OpenMetrics and OpenTelemetry Fit Within the Observability Ecosystem
    • OpenTelemetry: The Key to Full-Stack Observability
    • Conclusion
    • Frequently Asked Questions

OpenTelemetry: A Unified Standard for Observability

OpenTelemetry is an observability platform that comes with its own set of tools, application programming interfaces (APIs), and software development kits (SDKs).

OpenTelemetry enables IT departments to analyze program performance and behavior by instrumenting, generating, collecting, and exporting telemetry data.
OpenTelemetry is comprehensive, as it incorporates the features of OpenTracing and OpenCensus.
OpenTelemetry establishes a standard for the transmission and collection of observability data. OpenTelemetry is an incubating project at the Cloud Native Computing Foundation (CNCF) to deliver vendor-neutral standardized libraries and application programming interfaces (APIs).
The information collected by these APIs and libraries will be transmitted to another location.
OpenTelemetry provides a common language for describing application behavior. This common language enables interoperable integration between existing and future observability tools.
As a result, OpenTelemetry makes it possible to collect and analyze large amounts of data across multiple applications.

How OpenTelemetry works

OpenTelemetry is a protocol designed specifically for gathering telemetry data and sending it to another system.
This protocol provides a way to measure performance and identify problems without writing code specifically for that purpose.
From beginning to end, the data life cycle encompasses a wide variety of operations. Following are the procedures performed by OpenTelemetry and the information it gathers:

  • Provides APIs to instrument your code, instructing various parts of your system on what metrics to collect and how to manage them
  • Consolidates data using software development kits (SDKs), then transfers it to another system where it may be processed and exported
  • Dissects the data, takes samples, applies filters to eliminate mistakes, and then enhances it with multi-source contextualization
  • Performs conversions and outputs the data
  • Performs additional filtering on the data in time-based batches and transfers the information to a preset backend

The most important data cannot be collected without first being ingested. This can be accomplished in two main ways:

1) Span ingestion: The information may be taken directly or indirectly, depending on the provider. Root spans and child spans are common components of a standard index of spans. Metadata, event details, and more are all included in this data, making it invaluable.

2) Local ingestion: This happens after the information has been stored securely in memory. Time series data and tags are frequently transmitted to the cloud for analysis in on-premises or hybrid deployments. Cloud databases are great for archiving massive amounts of data, many of which may be subject to confidentiality or proprietary requirements.

OpenTelemetry’s advantages

Not all applications use the exact same data collection mechanism or have the same standard for presenting the data. Developers and SREs trying to gauge an app’s health may find this inconsistency a nuisance.

When it comes to integrating telemetry into cloud-native apps, OpenTelemetry has become the de facto standard. The reason is that it frees up resources, enabling businesses to focus on delivering new features rather than creating a system for collecting crucial application data.

OpenTelemetry enables developers to collect, store, analyze, visualize, and act upon data generated by distributed systems.

In addition to enabling developers to monitor and debug distributed applications, it also helps organizations understand how their distributed applications perform. By analyzing these metrics, they can determine whether to increase or decrease their current infrastructure size.

OpenTelemetry aims to create a common language for describing metrics across different platforms and languages.

As a result, developers can use a single set of tools to collect data from multiple systems.

So, for example, if you wanted to measure performance across various web servers, you could instead just run a script once and get the same data from every platform instead of needing to write code to gather data from each one

OpenTelemetry offers several benefits over existing solutions, including:

  • No need to install any software on endpoints
  • The ability to collect telemetry from multiple sources (e.g., application servers, databases)
  • Support for various languages and frameworks
  • Simple API calls to collect telemetry
  • Easy integration with third-party services
  • Built-in support for storing collected data
  • Extensible architecture
  • Works well with Kubernetes
  • Allows developers to ingest data into any system quickly

Is OpenTelemetry free?

Yes, Google’s OpenTelemetry is an open-source software project aiming to help programmers build distributed systems without cost or restrictions.

OpenMetrics: A Standard for Transmitting Cloud-Native Metrics

OpenMetrics is a free toolkit for collecting and reporting cloud-native application metrics. The toolkit includes a web dashboard, API, and command line interface (CLI).

OpenMetrics’ goal is to provide a standard API for collecting and reporting application performance metrics.

This opens up many possibilities for companies who want to monitor their infrastructure, including large enterprises and startups.

By using OpenMetrics, these organizations can save thousands of hours of development time and millions of dollars that would otherwise be spent on building their solutions.

Metrics can be gathered from any cloud provider, such as Amazon Web Services, Google Compute Engine, Microsoft Azure, and others. They can also report those metrics to third parties via APIs and web dashboards.

OpenMetrics is built on Prometheus, a popular monitoring system many large companies use. OpenMetrics uses Prometheus’ query language to collect metrics from various sources.

How OpenMetrics works

OpenMetrics supports two types of metrics: those that report application performance and those that report system performance.

Application performance includes requests per second, latency, throughput, errors, and exceptions.

System performance includes CPU utilization, memory usage, disk space, network bandwidth, and several containers.

OpenMetrics is designed to be simple to implement and integrate with existing applications. This simplicity makes it suitable for any application, including web apps, APIs, and command line tools.

OpenMetrics has three main components:

  • A client library that allows you to write code in your preferred programming language.
  • An HTTP endpoint that receives client metrics and sends them to a backend service.
  • A backend service that stores metric data and provides access to it through a RESTful API.


When using OpenMetrics, you’ll get detailed data about each metric across multiple dimensions.

For example, you can see how much time was spent waiting for resources, how long each request took, and how many errors occurred during processing.

Where OpenMetrics and OpenTelemetry Fit Within the Observability Ecosystem

The critical difference between these two tools lies in their approach to observability.

While both platforms offer similar functionality, OpenTelemetry focuses on providing visibility into the internal workings of applications, while OpenMetrics offers a broader view of the observability ecosystem.

For example, OpenTelemetry exposes telemetry data from individual components within an application, allowing you to monitor performance over time. 

In contrast, OpenMetrics allows users to query and analyze data from multiple sources, including logs, monitoring systems, and other services.

OpenTelemetry is also widely used during the rollout of updated programs and services. It ensures that new features work before they’re released.

The integration of these methods opens up a wealth of data never before available to programmers.

For example, you can use OpenMetrics to correlate events across disparate systems and identify potential bottlenecks.

Ultimately, the choice between these two technologies depends on which aspects of observability matter most to you.

Is it important for you to know how your applications spend their time? Then OpenTelemetry is probably right for you.

Are you interested in understanding how your applications behave under load? Then OpenMetrics is probably a good choice.

OpenTelemetry: The Key to Full-Stack Observability

OpenTelemetry enables full-stack observability by providing a familiar interface between application code and infrastructure monitoring systems.

Any developer can use OpenTelemetry to add observability to their own software.

OpenTelemetry provides a simple API that allows developers to instrument their software to collect telemetry data at runtime easily.

Developers can then query this data via HTTP requests and visualize it through web dashboards.

OpenTelemetry supports many different types of metrics, including CPU usage, memory consumption, network bandwidth, disk space, and file system access. It also includes support for tracing, profiling, and logging.

While OpenTelemetry is still relatively young, it already offers powerful capabilities for building observability into applications.

Conclusion

In conclusion, OpenTelemetry provides tools for building observability systems, whereas OpenMetrics focuses on providing a familiar interface for monitoring metrics from various sources.

Both approaches offer benefits and drawbacks, but OpenTelemetry provides a unique combination of features that makes it ideal for both use cases.

We hope this guide helps you understand the differences between OpenTelemetry and OpenMetrics.

Frequently Asked Questions

How do the three pillars of observability relate to OpenTelemetry?

The three pillars of observability are infrastructure, data collection, and analysis.

OpenTelemetry provides the infrastructure necessary to collect, store, and analyze data. It enables developers to collect and store data about their applications and servers without writing any custom software.

It also lets them analyze the data they collect to gain insight into how their apps perform.

By combining the three pillars of observability, OpenTelemetry can give developers access to the data they need to improve their apps’ performance.

What is OpenTelemetry used for?

OpenTelemetry is a set of tools designed to give developers visibility into the performance of their applications. These tools include Prometheus, Jaeger, and Zipkin.

These tools collect data about application behavior and send it to centralized servers calledcollectors.

OpenTelemetry offers a unified interface for gathering data from your infrastructure’s many components. For example, it allows you to see exactly how much memory or disk space your application uses, how many requests it processes per second, and how many errors occur during execution.

You can gain insight into how your system behaves under load and identify bottlenecks with access to this kind of data.

When should I use a collector?

Exporters for standard backends and OTLP are available for most language-specific instrumentation libraries. This allows for quick experimentation with various setups.

You may wonder why someone prefers a collector over directly sending data to a backend.

For trying out and getting started, sending your data directly is an excellent way to get value quickly – especially for smaller-scale environments where you don’t want to spend resources configuring and running collectors.

In addition, you can often get good results without a collector in a development or testing environment.

However, in most cases, we advise employing a collector together with your services because it expedites the offloading of data. In addition, the collector can take charge of additional handling such as retries, batching, etc.

What are the future plans for OpenTelemetry?

While the project’s primary focus is on telemetry, the team behind the OpenTelemetry project is interested in supporting loggers, observability tools, and monitoring solutions.