# 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](https://betterstack.com/community/guides/monitoring/prometheus-metrics-explained/) 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.

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## 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](https://betterstack.com/community/guides/observability/what-is-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.

| Feature                          | OpenTelemetry                                                                             | Prometheus                                                                                |
| -------------------------------- | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| Scope of Functionality           | Unified framework for metrics, logs, and traces; ideal for distributed systems.           | Time-series metrics collection, storage, and querying; excels in system-level monitoring. |
| Instrumentation                  | Automatic and manual instrumentation options; supports multiple languages.                | Primarily manual instrumentation; extensive client libraries and exporters.               |
| Data Collection                  | Push and pull models; typically push-based.                                               | Pull-based model; Prometheus server scrapes metrics.                                      |
| Query Language and Visualization | No built-in query language; relies on backend capabilities.                               | PromQL for querying and analysis; integrates with Grafana and other visualization tools.  |
| Integration and Extensibility    | Growing ecosystem of integrations and exporters; vendor-neutral and modular architecture. | Mature ecosystem with extensive integrations; text-based format fosters compatibility.    |
| Community and Ecosystem          | Newer but rapidly growing community; stable and production-ready.                         | Large and active community; mature, widely adopted, and reliable.                         |

## 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](https://betterstack.com/community/guides/observability/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](https://prometheus.io/docs/instrumenting/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](https://github.com/open-telemetry/opentelemetry-collector/tree/main/cmd/builder)
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!