OpenMetrics vs OpenTelemetry: A Detailed Comparison

When it comes to monitoring and observability, two of the most discussed standards are OpenMetrics and OpenTelemetry. While both are designed to collect and transmit metrics, they have distinct goals, use cases, and communities driving their development. 

In this guide, we'll break down what each of these projects is, how they compare, and how they fit into your monitoring stack.

What is OpenMetrics?

OpenMetrics is a specification for the collection and transmission of metrics, aimed at providing a consistent format that makes it easier to collect, store, and process performance data across various systems.

Originally developed by Prometheus, OpenMetrics focuses on standardizing the metrics format for more efficient exchange across monitoring tools and systems.

The OpenMetrics specification is all about metrics. It's concerned with defining how metrics are structured, making it easier for tools like Prometheus, Grafana, and other monitoring solutions to understand and use these metrics.

Some key features of OpenMetrics include:

• A standardized text format for representing time series data.
• Efficient transmission of metrics between systems.
• Compatibility with Prometheus, one of the most widely adopted monitoring solutions.

Although OpenMetrics was created with Prometheus in mind, it has since become a broader standard that can be adopted by other systems in the observability ecosystem.

What is OpenTelemetry?

OpenTelemetry is a larger observability framework that goes beyond just metrics. It includes support for tracing, logs, and metrics, providing a unified standard for collecting and exporting data across multiple sources. OpenTelemetry was designed to be a comprehensive solution for modern distributed systems, supporting end-to-end observability.

OpenTelemetry’s main strength is its flexibility. It can be used in conjunction with a wide range of backends, including Prometheus, Jaeger, Zipkin, and others. This open-source project is governed by the Cloud Native Computing Foundation (CNCF), which ensures that it has broad industry backing.

Key features of OpenTelemetry:

• Unified API for metrics, traces, and logs.
• Language support for a variety of programming languages, including Java, Python, Go, and others.
• Exporters for sending data to a variety of backends (including Prometheus, Jaeger, and Zipkin).

OpenTelemetry allows teams to gather a rich set of data, giving a fuller picture of how an application behaves under different conditions. However, while it covers all observability signals, it’s more complex compared to OpenMetrics, which is narrowly focused on metrics.

Key Differences Between OpenMetrics and OpenTelemetry

While both projects aim to make monitoring easier, their scopes, functionalities, and use cases vary significantly.

Here's a more detailed comparison of the two.

1. Scope and Focus

OpenMetrics is a specification primarily concerned with metrics. It defines a standardized format for representing and transmitting time series data, making it easier for systems to consume and process metrics. It focuses solely on metrics, ensuring that the data is structured optimally for storage and transport.

• OpenMetrics is great for teams that are working with metrics-heavy use cases and need standardized formats for efficient data exchange.
• The format is highly optimized for Prometheus, which makes it the natural choice if you're already in the Prometheus ecosystem.

OpenTelemetry, on the other hand, is a much broader observability framework. It spans metrics, tracing, and logs, providing a unified standard for collecting, processing, and exporting data from applications, services, and infrastructure.

OpenTelemetry is not just about collecting data; it’s about providing a complete observability solution for distributed systems.

• OpenTelemetry integrates all types of telemetry: metrics, traces, and logs, into a single framework.
• It aims to offer a comprehensive view of your systems, covering everything from performance metrics to service-level interactions and logs.

2. Adoption and Ecosystem

OpenMetrics has significant adoption within the Prometheus ecosystem. Since Prometheus was the project that initiated OpenMetrics, it integrates easily with Prometheus, making it the go-to solution for those already using Prometheus for monitoring.

• Prometheus natively supports OpenMetrics, which means the two tools are highly compatible and work together with minimal setup.
• It's most beneficial for teams that are committed to Prometheus and need a reliable and consistent way to export and query metrics.

On the other hand, OpenTelemetry has much broader support across various observability tools, such as Jaeger, Zipkin, Prometheus (for metrics), Datadog, and more.

• OpenTelemetry is widely adopted and backed by the Cloud Native Computing Foundation (CNCF), with strong support from various observability vendors.
• It is often preferred by organizations that use a multitude of monitoring tools and need a unified framework to manage all their observability data.

3. Use Cases

OpenMetrics shines in use cases where the primary focus is on metrics. If your monitoring solution revolves around time series data and you're using or planning to use Prometheus, OpenMetrics is an ideal fit.

• Metrics-only environments: If you're looking to collect only performance metrics (e.g., CPU usage, memory consumption, application response times), OpenMetrics is perfectly suited for this.
• OpenMetrics is designed for teams that don’t need tracing or logs and are focused on simplified, efficient metric collection.

OpenTelemetry, on the other hand, is designed for more complex environments, especially where full-stack observability is needed. It is suitable for teams who need tracing, logs, and metrics to understand the health of their systems at a deeper level.

• End-to-end observability: If your architecture involves complex interactions between multiple services or microservices, OpenTelemetry can help you understand these interactions, track performance with tracing, and gather logs for more in-depth diagnostics.
• Distributed systems: OpenTelemetry is ideal for cloud-native and distributed applications where understanding how components communicate and perform is critical.

4. Flexibility

OpenTelemetry offers greater flexibility because it handles all forms of observability data—metrics, traces, and logs. It allows you to choose which data you need and how you want to send it to different backends.

• You can export data to multiple backends (e.g., Prometheus for metrics, Jaeger for traces, Elasticsearch for logs) with minimal effort.
• OpenTelemetry allows you to mix and match observability signals based on the needs of your system.

OpenMetrics, on the other hand, is more specialized. It is focused solely on metrics and has fewer options for integration beyond metrics-focused systems like Prometheus. While it's great for metrics collection, it doesn't provide the flexibility needed to handle tracing or logging data.

• OpenMetrics is ideal for metric-specific use cases but is less flexible for teams looking to extend observability into traces or logs.
• It’s best used in environments where metrics are the priority.

5. Complexity

One of the major differences between OpenMetrics and OpenTelemetry is their complexity.

OpenTelemetry can be quite complex, especially when you're looking to implement full-stack observability. Setting up OpenTelemetry often involves integrating multiple components for different observability signals—metrics, logs, and traces. This process requires more configuration and management.

• OpenTelemetry requires configuring agents and collectors for different types of telemetry.
• You need to deal with context propagation across services for tracing, configure sampling strategies, and ensure data flows correctly between different telemetry signals.

However, this complexity comes with its advantages. By offering a unified framework for all observability signals, OpenTelemetry makes it easier to manage diverse observability requirements over time, especially as systems scale.

In contrast, OpenMetrics is much simpler to use. Since it focuses only on metrics and is closely integrated with Prometheus, the setup and management are straightforward. There are fewer moving parts and less configuration needed to collect and export metrics.

• If you're just concerned with metrics collection and you're in a Prometheus-heavy environment, OpenMetrics is easier to configure and maintain.
• However, its simplicity comes at the cost of missing out on full-stack observability.

Why OpenMetrics Matters for Prometheus Users

For organizations heavily invested in Prometheus, adopting OpenMetrics is a no-brainer. Since Prometheus uses OpenMetrics as its native format, it provides a hassle-free way to integrate monitoring and alerting systems.

Using OpenMetrics with Prometheus has the following advantages:

• Standardized format: OpenMetrics offers a uniform approach to exporting and consuming metrics.
• Improved interoperability: OpenMetrics improves interoperability between different monitoring tools that support the specification.
• Easier data management: Prometheus users can manage and query metrics more effectively, ensuring consistent and reliable data handling.

The Role of OpenTelemetry in a Modern Observability Stack

While OpenMetrics is a great choice for organizations relying on Prometheus, OpenTelemetry plays a more central role in a broader observability stack.

OpenTelemetry's ability to unify traces, logs, and metrics under one roof makes it especially valuable for organizations dealing with complex, distributed systems.

Here are some reasons why OpenTelemetry should be considered:

• Comprehensive observability: It gives a full view of how services interact and perform.
• Easy integration with existing tools: OpenTelemetry is compatible with many existing observability tools, including Prometheus and Jaeger.
• Ecosystem support: OpenTelemetry has the backing of the CNCF, making it an industry-standard tool for observability in cloud-native environments.

When to Choose OpenMetrics or OpenTelemetry

Consider this hypothetical scenario: You're working at a fast-growing SaaS company with microservices running on Kubernetes. You need to monitor performance, but your focus is primarily on metrics for scaling purposes.

In this case, using OpenMetrics with Prometheus would be the ideal choice. It's simple, efficient, and integrates well with the monitoring tools you're already using.

However, as your services grow and you need to trace requests across microservices and analyze logs, OpenTelemetry would be the right move.

With its broader support for traces and logs, it offers a complete observability solution. OpenTelemetry also allows you to maintain metrics alongside traces and logs, helping you connect all the dots when diagnosing performance issues.

Lesser-Known Benefits of OpenTelemetry

While much has been written about OpenTelemetry’s wide scope, some lesser-known aspects can help with decision-making:

• Built-in context propagation: OpenTelemetry includes features for automatic context propagation in distributed systems. This means that your tracing data can automatically carry context across different services, saving you time in setting up context tracking manually.
• Sampling strategies: OpenTelemetry provides built-in mechanisms for adaptive sampling, which allows you to control how much tracing data is collected. This can help balance performance with observability.
• Rich community support: OpenTelemetry has a large community of developers and users who contribute to its ongoing improvement. This means better documentation, frequent updates, and new features coming from across the industry.

A Quick Comparison of OpenMetrics vs OpenTelemetry

Conclusion

OpenMetrics is ideal if your main goal is to collect and export metrics, particularly if you’re a Prometheus user. OpenTelemetry, however, shines when you need a complete observability solution that spans metrics, logs, and traces.

Remember, these tools aren't necessarily mutually exclusive. Many organizations use both—OpenMetrics for metrics and OpenTelemetry for tracing and logging—making them complementary pieces of a well-rounded observability strategy.