Prometheus vs. OpenTelemetry: Choosing Your Observability Tool

Prometheus and OpenTelemetry are two prominent open-source observability projects under the Cloud Native Computing Foundation (CNCF), each designed to equip developers with comprehensive monitoring tools tailored for modern cloud-based architectures.

Prometheus revolutionized the monitoring landscape with its robust time-series metrics collection, powerful querying language, and alerting capabilities. Meanwhile, OpenTelemetry emerged as a unified standard for instrumenting and collecting telemetry data—encompassing metrics, logs, and traces—across diverse services and platforms, fostering vendor-agnostic observability.

In this article, we will delve into the intricacies of both projects, comparing their architectures, data models, strengths, and ideal use cases. By understanding their unique approaches and how they complement or contrast with each other, you will be better equipped to navigate the dynamic landscape of cloud observability and choose the most suitable tools for your specific needs.

What is Prometheus?

Prometheus is an open-source monitoring system that specializes in gathering and analyzing metrics. Established in 2012 by developers at SoundCloud and later adopted by the CNCF in 2016, it has grown to have a large community of users and contributors.

Prometheus is distinguished by its simple yet powerful data model and its query language, PromQL, which enables detailed analysis of application and infrastructure performance. This system is designed to integrate seamlessly with various programming environments and has extensive support for third-party software through its exporters, enhancing its adaptability across different technologies like Kubernetes and Docker.

Notably, Prometheus focuses strictly on metrics without venturing into other monitoring domains, preferring to leave those areas to other specialized tools. Its architecture is robust yet simple, capable of handling millions of data points per second, and is structured to work effectively within existing infrastructures.

Key Components of Prometheus

Prometheus is built around several key components that work together to provide a comprehensive metrics-based monitoring solution:

• Prometheus server: The core component that scrapes and stores time-series data from configured targets at specified intervals. It also provides a simple web interface for querying and analyzing collected metrics data.

• Client libraries: These libraries are used to instrument your own code or third-party applications, enabling them to expose metrics in the Prometheus format.

• Exporters: These are tools that allow Prometheus to collect metrics from systems that don't natively support its metric format. Examples include Node exporter for hardware and OS metrics, and others for services like MySQL, Apache, and Kafka.

• Pushgateway: This acts as an intermediary for allowing short-lived jobs to push metrics to Prometheus even if they don't run long enough for Prometheus to scrape them.

• Alertmanager: Manages alerts sent by the Prometheus server. It handles deduplication, grouping, and routing of alert notifications to the correct receiver.

• PromQL (Prometheus Query Language): A flexible query language that allows you to select, aggregate, and visualize time-series data in real time.

• Service discovery: Automates the discovery of networked services and hosts so that Prometheus begin monitoring new instances as they come online, which is useful in dynamic environments like Kubernetes, where services are constantly being created and destroyed.

What is OpenTelemetry?

OpenTelemetry is an open-source framework aimed at providing standardized tools for instrumenting, collecting and exporting telemetry data, such as metrics, logs, and traces. It originated from the merger of OpenCensus and OpenTracing, two prior initiatives aimed at simplifying observability.

Its primary objective is to offer a unified and vendor-neutral approach for observability across various applications and infrastructure components. It also provides comprehensive support for popular programming languages such as Java, Python, Go, and JavaScript, to help simplify adoption across diverse projects.

OpenTelemetry offers both automatic instrumentation for common libraries and frameworks, as well as manual instrumentation options for greater customization. Its standardized APIs and SDKs ensure consistency in data collection and processing across different telemetry types, facilitating unified observability practices. It is also designed to be modular and adaptable, allowing it to fit into various architectures and scale with growing systems.

Key components of OpenTelemetry

• API and SDKs: The API provides a standard interface for instrumenting applications to generate telemetry data, while SDKs offer language-specific implementations of the API, making it easier for developers to add instrumentation to their code.

• Collector: The Collector is a central component that receives, processes, and exports telemetry data in various formats (including Prometheus). It can be deployed as an agent alongside applications or as a standalone service.

• Instrumentation libraries: These libraries provide pre-built instrumentation for popular frameworks and libraries, automatically generating telemetry data without requiring manual code changes.

• Exporters: Exporters are responsible for sending telemetry data to different backends or analysis tools. OpenTelemetry supports a wide range of exporters for various observability platforms and formats.

• Semantic conventions: These define standard attribute names and values for telemetry data, ensuring consistency and interoperability across different components and systems.

OpenTelemetry vs Prometheus

With a firm grasp of both OpenTelemetry and Prometheus, let's dive into a head-to-head comparison of their metrics capabilities. By analyzing their strengths and weaknesses side-by-side, you can better discern which tool is the right fit for various observability scenarios.

1. Scope of functionality

OpenTelemetry focuses on providing a unified standard for collecting, processing, and exporting telemetry data across various types, including metrics, logs, and traces.

It is a broader project that encompasses the entire telemetry spectrum, making it more of a unified observability framework than just a monitoring tool. In particular, its emphasis on distributed tracing makes it ideal for understanding complex interactions between services in microservice architectures.

Prometheus, on the other hand, concentrates specifically on time-series metrics collection, storage, and querying. Its strengths lie in monitoring system-level metrics like CPU usage, memory, and request latency, making it well-suited for infrastructure and application performance monitoring. It excels in the metrics domain with its powerful query language (PromQL) and extensive ecosystem of integrations.

2. Instrumentation

Prometheus primarily relies on manual instrumentation. This means you need to explicitly add code to your applications using Prometheus client libraries to define and expose metrics. While this approach offers flexibility and control over the metrics collected, it can be time-consuming for large or complex applications.

However, Prometheus benefits from a wide range of client libraries that automatically expose metrics for popular programming languages and frameworks. It also has a large number of exporters available for collecting metrics from third-party systems, making it easier to instrument existing applications and infrastructure.

OpenTelemetry offers both manual and automatic instrumentation options for greater flexibility. Automatic instrumentation uses pre-built instrumentation libraries that can automatically instrument popular frameworks and libraries, reducing the manual effort required.

For more fine-grained control, OpenTelemetry also provides manual instrumentation through its API and SDKs. This allows you to define custom metrics, logs, and traces to capture specific aspects of your application's behavior.

3. Data collection

Prometheus follows the pull-based data collection model where it actively scrapes metrics data from instrumented endpoints at regular intervals. This model is simple and reliable but can be less flexible in dynamic environments.

OpenTelemetry supports both push and pull models for data collection and can handle more types of data beyond metrics. It typically uses a push-based model, where instrumented applications actively send telemetry data to the OpenTelemetry collector or directly to an observability backend.

4. Query language and visualization

Prometheus uses PromQL, which allows you to write highly specific queries to extract insights from metrics data. While it also offers basic analysis, visualization, and alerting features, it is frequently integrated with Grafana, for richer and more customizable dashboards. It can also be paired with other open-source or proprietary tools to suit your preferences and requirements.

OpenTelemetry does not have its own query language since it focuses on collecting and transmitting data rather than analyzing it. Querying, visualization, and analysis are typically handled by whichever backend it is configured to send data to.

5. Integration and extensibility

Prometheus has a wide range of exporters that enable it to monitor a variety of third-party applications and services that do not natively emit Prometheus metrics. Exporters for many popular tools are already available, and you can easily create your own where necessary.

OpenTelemetry also provides receivers for ingesting telemetry data in different formats (including Prometheus), and exporters for sending the ingested and processed data to one or more observability backends. There's a vast number of integrations available so you're advised to create a custom OpenTelemetry Collector builds that includes only the specific components you need.

6. Community and ecosystem

Both projects are under the umbrella of the CNCF and have strong community support. OpenTelemetry is newer and still evolving, but it is already considered stable and production-ready for many use cases.

Prometheus has been around longer and is widely adopted with a robust ecosystem of tools, integrations, and a dedicated user base. This translates to ample support, extensive documentation, numerous tutorials, and a wealth of shared knowledge and experiences.

It has been widely adopted by organizations of all sizes and has proven its reliability in production environments. However, OpenTelemetry is quickly catching up and is becoming the preferred choice for organizations seeking a unified approach to observability.

How to choose between the two

Choosing between Prometheus and OpenTelemetry depends on several factors, including your specific observability needs, existing infrastructure, and long-term goals. Here's a breakdown of key considerations:

When to choose Prometheus

Prometheus shines when your primary focus is on collecting and analyzing time-series metrics data. Its robust query language, vast ecosystem of exporters for diverse data sources, and seamless integration with metric analysis tools make it an excellent choice for monitoring system and application performance.

If you already have Prometheus deployed or have heavily invested in tools that integrate well with it, it may be more practical to continue utilizing it as it is a well-established project supported by a large and active community, providing a stable and reliable solution for your monitoring needs.

When to choose OpenTelemetry

On the other hand, OpenTelemetry is the tool of choice if you need a comprehensive observability solution that encompasses metrics, logs, and traces. Its vendor-neutral approach and standardized data model make it adaptable to various backends and tools, offering flexibility and avoiding lock-in.

For those working with microservices or distributed systems, OpenTelemetry's strong support for distributed tracing is invaluable for understanding complex service interactions. Furthermore, OpenTelemetry's modular architecture and support for multiple languages and platforms make it highly extensible and adaptable to diverse environments and use cases. It can also smoothly integrate with existing observability tools (including Prometheus).

Using both together

Using Prometheus and OpenTelemetry are not mutually exclusive. In fact, they can complement each other very well. You can use OpenTelemetry to collect telemetry data from your applications and then export the metrics to Prometheus for storage and analysis.

Or, if you're already using Prometheus to instrument your applications, you can leverage the OpenTelemetry Collector as a bridge to send your Prometheus metrics to other analysis tools or observability backends. This approach allows you to maintain your existing Prometheus setup while gaining the flexibility and standardization offered by OpenTelemetry.

Final thoughts

Ultimately, the choice between OpenTelemetry and Prometheus depends on your specific needs and priorities. Consider your monitoring goals, existing infrastructure, and desired level of flexibility.

By carefully evaluating these factors and understanding the unique strengths of each tool, you can make an informed decision that empowers you to achieve comprehensive observability and optimize the performance and reliability of your systems.

Thanks for reading!

Article by

Ayooluwa Isaiah

Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.

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