OpenTelemetry vs. Prometheus: Key Differences & Use Cases

While both OpenTelemetry and Prometheus are solutions that can help teams implement an open, flexible approach to metrics data collection, there are important differences between them – starting with the fact that they are fundamentally different types of solutions. 

For details on how they differ and what to use when, keep reading as we unpack everything you need to know about OpenTelemetry and Prometheus.

What is OpenTelemetry?

OpenTelemetry is an observability framework designed to bring consistency to the process of collecting telemetry data. Through standardized instrumentation libraries, OpenTelemetry makes it possible to expose logs, metrics, and traces from applications in a way that any OpenTelemetry-compatible monitoring or observability tool can understand.

This means that with OpenTelemetry, you can monitor and observe applications in a consistent way regardless of which monitoring and observability tools you choose.

Pros and cons of OpenTelemetry

The main advantage of OpenTelemetry is that it standardizes the collection of telemetry data. This means, again, that you can use any OpenTelemetry-compliant observability tool to monitor or observe your applications, provided OpenTelemetry instrumentation is in place for the apps. It also means you don’t have to worry about writing custom code to expose metrics, logs, and traces for each app. Potential disadvantages of OpenTelemetry are that it only supports certain programming languages, and its features are more robust for some languages than others. OpenTelemetry also doesn’t support all data types. 

In general, OpenTelemetry has become a massively popular solution for standardizing telemetry data collection. But it may not be a good fit for scenarios where your applications use programming languages that OpenTelemetry doesn’t support or you need to work with custom data types.

What is Prometheus?

Prometheus is an open source monitoring tool. Its primary purpose is to collect metrics data from applications and generate alerts based on anomalies – such as high rates of CPU or memory usage.

Pros and cons of Prometheus

As a monitoring tool, one of Prometheus’s key advantages is that it is fully open source, and thus free to use. It’s also highly flexible and customizable, and it supports the monitoring of virtually any type of application.

On the other hand, a major limitation of Prometheus – at least compared to some other observability platforms – is that Prometheus only supports metrics. It’s not a logging or tracing solution. As a result, if you want complete context into application performance, you’d need to use other tools (like Logstash for logging and Jaeger for tracing) in conjunction with Prometheus to collect other types of telemetry data, then correlate that data with Prometheus metrics.

OpenTelemetry vs. Prometheus: Key differences

OpenTelemetry and Prometheus are similar in that both help enable an open, flexible approach to observability. However, they are fundamentally different types of solutions, and they differ in a variety of important ways.

Main function

The most significant difference between OpenTelemetry vs. Prometheus is that OpenTelemetry is a telemetry data collection framework, whereas Prometheus is a monitoring tool.

Thus, OpenTelemetry and Prometheus serve different functions and needs. OpenTelemetry’s main purpose is to help you collect telemetry data, while Prometheus focuses on collecting and alerting based on Prometheus metrics data. OpenTelemetry can support metrics collection using a Prometheus receiver or other methods, but OpenTelemetry’s scope is broader than Prometheus’s

Data types supported

Another key difference between OpenTelemetry and Prometheus is that OpenTelemetry supports all common types of telemetry data – metrics, logs, and traces – whereas Prometheus only supports metrics.

As noted above, there are limitations in the types of data that OpenTelemetry can collect in some cases, especially when you’re working with apps written in languages with limited OpenTelemetry support. Still, in most scenarios, OpenTelemetry supports a broader range of data types than Prometheus.

Collection model

Prometheus relies on a pull model to collect Prometheus metrics. This means it pulls metrics in from the applications you want to monitor.

In contrast, OpenTelemetry can support both pull and push models. You can pull metrics into OpenTelemetry-compatible monitoring tools using methods like the Prometheus receiver, or push them from your applications to the tools.

Scalability and performance

OpenTelemetry and Prometheus are both scalable in the sense that they can observe multiple applications or services, and there are no hard limits on the amount of data you can collect.

However, Prometheus can be more challenging to scale in some ways, largely because it’s designed to be deployed on a single Prometheus server, without built-in features like multi-tenancy. This means that Prometheus performance is constrained by the availability of resources on the server hosting your Prometheus instance. That said, it is possible to implement advanced Prometheus architectures that include multiple server replicas, which can help to scale the tool.

OpenTelemetry isn’t typically subject to scalability challenges because you can easily deploy additional collectors (the software responsible for collecting data) or push data directly into monitoring tools.

Community and adoption

OpenTelemetry and Prometheus are both widely popular solutions with high adoption rates – a reflection of the fact that they’re open and flexible.

That said, Prometheus likely has fewer users overall. This is because there are many other monitoring and observability tools available that do the same thing as Prometheus. In contrast, there is no real alternative to OpenTelemetry (except arguably eBPF, as we explain toward the end of this article).

OpenTelemetry and Prometheus use cases

Because of the differences described above, OpenTelemetry and Prometheus cater to different use cases.

When to use OpenTelemetry

In general, OpenTelemetry is ideal for use cases like the following:

• Comprehensive monitoring of cloud-native applications and distributed systems: Because OpenTelemetry can collect all key types of telemetry data, it’s useful for monitoring complex cloud-native apps or distributed systems where you need to compare OpenTelemetry metrics, logs, and traces to track down the root cause of performance issues.
• Distributed tracing: While OpenTelemetry is not the only way to collect traces, it offers robust tracing support, making it a popular framework for tasks like Kubernetes tracing.

When to use Prometheus

Meanwhile, key use cases for Prometheus include:

• Infrastructure monitoring: Because Prometheus focuses on metrics collecting and alerting, it’s an ideal solution for infrastructure monitoring use cases where your main goal is to detect anomalies like high CPU usage by a server.
• Application performance monitoring (APM): Likewise, for APM use cases where Prometheus metrics analysis is the primary methodology of detecting issues, Prometheus can be a useful tool. It may be less ideal, however, for monitoring complex cloud-native apps and distributed systems, where Prometheus metrics alone may not suffice for managing performance.
• Alerting: Because Prometheus offers built-in alerting, it’s a handy solution if you want both metrics collection and alerting in a single tool.

Benefits of integrating OpenTelemetry with Prometheus

Although OpenTelemetry and Prometheus serve different functions, they’re not necessarily either-or solutions – so thinking in terms of “OpenTelemetry vs. Prometheus” isn’t always the best approach.

You can use them together – and doing so is often a smart way to streamline observability.

By combining OpenTelemetry with Prometheus, you get benefits like:

• Improved observability: Prometheus can help with alerting based on Prometheus metrics, while OpenTelemetry rounds out visibility by allowing you to collect logs and traces as well, then analyze them using OpenTelemetry-compatible tools.
• Enhanced scalability and performance: When combined, OpenTelemetry and Prometheus can improve the scalability and performance of observability operations. This is because OpenTelemetry (which is more inherently scalable, as noted above) can collect data in cases where your Prometheus server becomes overwhelmed.
• Flexible data visualization: By using both solutions in tandem, you enjoy more flexibility in how you visualize data. For example, you could set up Grafana to visualize Prometheus metrics, as well as logs and traces imported via OpenTelemetry.

How to integrate OpenTelemetry with Prometheus in simple steps

Integrating OpenTelemetry with Prometheus involves collecting telemetry data using OpenTelemetry and exposing it in a Prometheus-compatible format for monitoring. Here are the basic steps.

1. Set up OpenTelemetry

First, instrument the OpenTelemetry libraries in your application. The way you do this varies depending on which programming language you use, but it’s often as simple as importing the relevant libraries.

For example, in a Python app, you could set up OpenTelemetry with:

from opentelemetry import metrics

2. Install the OpenTelemetry collector

Next, install the OpenTelemetry collector. You can run it using Docker with:

docker run -p 4317:4317 -p 8888:8888 -p 9464:9464 \
--name otel-collector otel/opentelemetry-collector:latest

You also need to configure the collector to send metrics to Prometheus. You can do this by creating a simple configuration file like the following (and saving it as otel-collector-config.yaml):

receivers:
  otlp:
	protocols:
  	grpc:
  	http:

processors:
  batch:

exporters:
  prometheus:
	endpoint: "0.0.0.0:9464"  # Port where metrics will be exposed

service:
  pipelines:
	traces:
  	receivers: [otlp]
  	processors: [batch]
  	exporters: []
	metrics:
  	receivers: [otlp]
  	processors: [batch]
  	exporters: [prometheus]

Finally, run the collector, being sure to reference your configuration file:

./otelcol --config=otel-collector-config.yaml

3. Install Prometheus

Prometheus is available as a package on most Linux distributions. For example, to install it on Ubuntu, run:

sudo apt install prometheus

However, before using Prometheus with OpenTelemetry, you need to add the OpenTelemetry collector endpoint to your Prometheus configuration (which is typically located at /etc/prometheus/prometheus). You can do this with the following code:

scrape_configs:
  - job_name: "otel-collector"
	static_configs:
  	- targets: ["localhost:9464"]

If Prometheus is already running, restart it for the new configuration to take effect.

At this point, Prometheus is configured to import data using OpenTelemetry. Note that in this example, we only configured our app to collect metrics using OpenTelemetry. If you wanted to collect traces and logs as well, you’d need to add other instrumentation code. You’d also have to direct logs and traces to other observability tools, since Prometheus only works with metrics.

Best practices for using Prometheus with OpenTelemetry

To get the greatest value when using Prometheus and OpenTelemetry together, consider best practices like the following.

Avoid redundant data collection

OpenTelemetry and Prometheus can both ingest metrics, but if you use them at the same time for this purpose by using an OpenTelemetry Prometheus receiver while also pulling data into Prometheus directly, you’re collecting data redundantly. A better approach is to use OpenTelemetry for logs and traces, while relying on Prometheus for metrics collection.

Deploy visualization tools

Neither OpenTelemetry nor Prometheus is a data visualization solution. To interpret your data, you’ll want to use a visualization tool like Grafana.

Manage data efficiently

OpenTelemetry and Prometheus both make it easy to collect telemetry data (and, in the case of OpenTelemetry, export telemetry data), but if you end up with too much data, it will be costly to store. It may also be challenging to analyze due to the scale of your data set. For these reasons, avoid collecting irrelevant data, and be sure to archive or delete dated data that you no longer need.

Plan for scalability

Because of the scalability limitations of Prometheus in particular, it’s important to plan ahead to ensure that the solution can scale with your needs so that you don’t find yourself unable to observe applications effectively due to the scale of your environment.

As for OpenTelemetry, while it’s typically easier to scale than Prometheus, you should still have a plan in place to ensure that the load that your collectors place on your environments doesn’t become a barrier to effective performance.

OpenTelemetry vs. Prometheus: Which solution is better for you?

While there are situations where OpenTelemetry is better than Prometheus, or vice versa, the best approach in many cases is to use both tools in tandem. This is particularly true if, again, you need to collect metrics, logs, and traces and analyze them simultaneously. Prometheus excels at handling metrics, while OpenTelemetry handles logs and traces as well.

That said, if you already have an observability tool in place that allows you to analyze and alert on metrics, logs, and traces, you may not need Prometheus. You can simply use OpenTelemetry to import all of the data into the platform.

Leveraging OpenTelemetry and Prometheus with groundcover

groundcover is an example of an end-to-end observability platform that does everything OpenTelemetry and Prometheus can do, and more to boot.

With groundcover, not only can you collect logs, metrics, and traces in a unified platform that includes visualizations and alerting, but you can also drill deep down into the data to identify the root cause of complex performance issues.

Even better, while groundcover fully supports OpenTelemetry as one way of collecting telemetry data, we also give you the option of using eBPF – which collects data via kernel-space programs, a hyper-efficient and hyper-secure approach. This means you get the best of both worlds – OpenTelemetry and eBPF – as observability options when you choose groundcover.

OpenTelemetry, Prometheus and the future of observability

As flexible, open observability solutions, OpenTelemetry and Prometheus are poised to play key roles in helping organizations manage application performance for years to come. But they are distinct tools that play distinct roles – and it’s important to know when you should use one solution, the other, or both at the same time.