OpenTelemetry vs Prometheus: Understanding the Differences

OpenTelemetry and Prometheus are two distinct and prominent tools in the observability landscape. Both are managed by the Cloud Native Computing Foundation (CNCF) and have some overlapping features—however, they are not direct competitors and are often used together.

In this article, I want to do my best to explain the differences between OpenTelemetry and Prometheus, paying particular attention to how they are practically used individually and in conjunction.

Understanding the High-Level Difference Between OpenTelemetry and Prometheus

A high-level distinction between OpenTelemetry and Prometheus could be defined by their missions.

The mission of OpenTelemetry is one of standardization: It translates metrics, logs, and traces—the fundamental building blocks of observability—into a standardized format that can be read and written by third-party tools.

Meanwhile, Prometheus is a data solution strictly for metrics. Prometheus scrapes metrics, standardizes them, and stores them. Some might frame Prometheus as an analytical database, but it does manage more of the process than a standard database would. However, from a strictly storage standpoint, Prometheus could be compared to other time series or OLAP databases like Timescale or ClickHouse.

Introduction to OpenTelemetry and Prometheus

OpenTelemetry and Prometheus are two popular open-source projects under the Cloud Native Computing Foundation (CNCF) umbrella. OpenTelemetry is a vendor-neutral open standard for instrumenting, generating, collecting, and exporting telemetry data, while Prometheus is a widely used monitoring and alerting tool in organizations. Both projects aim to provide observability tools for application monitoring, but they have different focuses and use cases. In this article, we will explore the key differences between OpenTelemetry and Prometheus, their benefits, and challenges when using them together.

How Do OpenTelemetry Metrics Work?

OpenTelemetry (or OTel) provides a centralized system for generating, collecting, and organizing all telemetry data. OpenTelemetry covers stack traces, logs, and metrics. This comprehensibility is important because it positions OpenTelemetry to be a unified standard in the same capacity as frameworks like OpenAPI and Netconf serve for APIs or networking.

OpenTelemetry has a curious history. It was born in 2019 when two projects—OpenCensus and OpenTracing—merged to unify data between APIs, SDKs, and other integrations. The conjoined product solved a problem that plagued observability: mismatched data across point observability solutions. OpenTelemetry ensures that telemetry data is kept consistent between sources.

The integration and utilization of OTel data with Prometheus enhance monitoring and observability within Kubernetes environments.

OpenTelemetry is a suite of components, ranging from an OpenTelemetry API, language-specific SDKs, and a dedicated data collector. These components enable OpenTelemetry to easily be queried, integrated with any language, and collect metrics from any source.

Because OpenTelemetry can easily export data to various vendors (e.g. HyperDX), it can also serve as a middle layer that exports data to Prometheus.

What Teams Use OpenTelemetry?

Some massive companies such as GitHub, Heroku, Shopify, eBay, ZocDoc, and Care.com (opens in a new tab) use OpenTelemetry. This includes various stacks, such as Ruby, Python, and Node.

How Do Prometheus Metrics Work?

At its core, Prometheus is a time series database. Time series data is simply data that is indexed by time, such as CPU utilization, monthly tickets, and weekly application traffic. In the context of developers, time series data defines metrics that describe an application’s health.

Prometheus metrics data is collected and stored by the Prometheus server, which gathers metrics from defined targets.

Prometheus also has a curious backstory—it was originally a project at SoundCloud that got open-sourced, and the Prometheus server was critical to detecting and preventing outages. SoundCloud naturally pushes a lot of data in and out of its servers; Prometheus was critical to detecting and preventing outages.

Prometheus’s primary data structure is collation of a metric’s name, label, timestamp, and value. Prometheus makes this data easily accessible via its own query language, PromQL. With PromQL, developers can query Prometheus data for dashboards to give themselves an indication of the application’s health. These can be organized into metrics such as:

• Counters: Numerical values that tick up, such as error count

• Gauges: Values that quickly rise and fall, such as memory usage

• Histograms: Events that fall into distribution buckets sorted by duration, response size, or any other arbitrary metric. These can be leveraged later to approximate percentiles.

Notably, Prometheus was originally designed for ephemeral storage—the default configuration deletes data that’s over 15 days old. Many teams don’t leverage it as a long-term storage solution, instead trusting platforms like CNCF’s in-house solution, Thanos. However, some teams might alter Prometheus’s defaults so it can store data for years.

Instead, Prometheus keeps recent data so that it can fire off alerts if a certain threshold is hit (e.g., CPU temperature is unsafe).

Prometheus also natively integrates with Kubernetes, making it easy to extend Prometheus’s data collection to complex systems deployed on multi-cloud environments.

Prometheus operates through a pull-based model to gather metrics from service endpoints, allowing multiple servers to pull metrics simultaneously from the same source.

What Teams Use Prometheus?

Some massive companies such as DigitalOcean, Grafana Labs, CoreOS, Ericsson, and, of course, Soundcloud use Prometheus.

Key Differences Between OpenTelemetry and Prometheus

OpenTelemetry and Prometheus have different design centers and use cases. OpenTelemetry is primarily focused on instrumentation and does not provide a backend for storing telemetry data. Instead, it forwards data to a backend vendor for storage, alerting, and querying. Prometheus, on the other hand, provides a time-series data store for storing metrics data and is widely used for monitoring and alerting. OpenTelemetry supports Prometheus in a Kubernetes environment, and Prometheus can accept OpenTelemetry Protocol (OTLP) metrics.

How Do OpenTelemetry and Prometheus Work Together?

OpenTelemetry easily integrates with Prometheus—OpenTelemetry’s SDKs can siphon metrics from Prometheus data, and Prometheus can ingest OpenTelemetry metrics in the Prometheus format. Prometheus exporters play a crucial role in enhancing interoperability between Prometheus and OpenTelemetry. Organizations can use OpenTelemetry to gather Prometheus metrics, showcasing the flexibility offered by Prometheus exporters.

However, their popularity isn’t equal. Almost all Prometheus users will leverage OpenTelemetry to standardize formats, while OpenTelemetry users will often use other tooling to manage Prometheus metrics. Understanding the differences between target labels in Prometheus and resource attributes in OpenTelemetry is crucial for effective prometheus metric visualization and querying. The collected data from both systems can be used to create comprehensive dashboards and graphs for monitoring and analysis.

Benefits of using OpenTelemetry and Prometheus together

Using OpenTelemetry and Prometheus together provides several benefits. OpenTelemetry’s SDKs allow tracking all observability signals in a single integrated telemetry library, while Prometheus provides a powerful querying language and alerting capabilities. By bridging metrics between OpenTelemetry and Prometheus, you can leverage the strengths of both systems and gain a more comprehensive understanding of your application’s performance. Additionally, the rich Prometheus exporter ecosystem is available for OpenTelemetry users, providing a wide range of options for exporting metrics to Prometheus.

Challenges and Considerations

While using OpenTelemetry and Prometheus together can be beneficial, there are also challenges and considerations to keep in mind. One of the main challenges is bridging metrics between the two systems, which can be complex and require careful configuration. Additionally, OpenTelemetry and Prometheus have different naming conventions and limitations, which can lead to surprises when translating metrics. Furthermore, OpenTelemetry does not provide a storage solution and must be paired with a separate backend system, which can add complexity to the overall architecture.

Conclusion

OpenTelemetry and Prometheus are both observability platforms that can be deployed individually or in sync. The choice between the two often depends on specific organizational needs, existing infrastructure, and future scalability requirements.