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Prometheus vs. Grafana in 2023: A detailed comparison

Prometheus and Grafana are two big names in the open-source world of observability. Both are widely liked and used, with vibrant, opinionated communities, and they routinely build on top of each other.

So, how do Prometheus and Grafan stack up against each other? In this blog, we'll compare them and examine -

  1. How their offerings overlap and differ

  2. How they perform against each other on a variety of criteria

  3. How they’re commonly used - together and separately, and why

Introduction to Prometheus and Grafana


Prometheus is a monitoring solution. An open-source project, it was started by SoundCloud in 2012 and has since gained immense popularity and traction. One reason for its widespread adoption is its seamless integration with Kubernetes. Many would argue that Prometheus is the de facto monitoring standard for a Kubernetes environment.

Prometheus offering

At its core, Prometheus is a time-series DB that uses a pull mode to fetch metrics from instrumented jobs. Coupled with its multidimensional data model and flexible query language, Prometheus allows devs to easily get, store, and work with metrics data.

  • Data Collection: Prometheus discovers and scrapes metrics from predefined targets, typically service endpoints or infra components.

  • Data Storage: Prometheus has a time-series DB that allows for highly efficient storage and querying of metrics data. For any given metric, data points are indexed by time and labeled, allowing for rich, multi-dimensional queries.

  • Querying with PromQL: Once data is stored, users can use PromQL (Prometheus Query Language) to retrieve and analyze metrics. It's a flexible query language allowing for precise slicing, dicing, and aggregation of data, ideal for deep performance analysis.

  • Visualization: While Prometheus comes equipped with a built-in visualization interface, it is basic and primarily intended for ad-hoc querying. For a richer, more robust visualization experience, Prometheus recommends using Grafana.

Take a look at a Prometheus Expression browser:

Prometheus expression browser
Prometheus expression browser snapshot

Image Source

In contrast, the Grafana visualization of Prometheus data is much richer

Grafana graph with time series data from Prometheus
A Grafana dashboard

Image Source


Grafana started as a visualization tool. Imagine having tons of data but no intuitive way to see or make sense of and navigate through it. Grafana was the answer to this, turning complex metrics data into understandable, interactive graphs.

Over the years, Grafana has evolved into a lot more and is now a full-stack observability platform. It not only helps users visualize data but also assists in collecting and aggregating it. In addition, Grafana can be used not just for metrics but also for other observability data (logs and traces).

See image below for the difference between Prometheus and Grafana.

Prometheus vs Grafana
Prometheus vs Grafana

In summary, the primary difference is that Prometheus is primarily a monitoring solution, while Grafana is a more comprehensive, full-stack solution that can be used across metrics, traces, and logs.

Prometheus vs. Grafana: Detailed assessment

Now that we understand what each of Prometheus and Grafana offers, let us compare them across the following criteria

  1. Core observability functions (Data collection, processing & storage)

  2. Scalability

  3. Querying

  4. Alerting

  5. Visualization (Visualization, UI/ UX, collaboration)

  6. Others (Documentation, ease of deployment, integrations, and pricing)

Summary assessment




Breadth of solution

(Only metrics)


( across metrics, logs, traces)

Data collection/ instrumentation




(also has logs/ traces; metrics agent similar to Prometheus)

Data storage

(purpose-built for metrics)


(across metrics, logs, traces; metrics DB built on top of Prometheus)



(Mimir more scalable)



(built-in AlertManager)

(slightly less performant)





(Built on PromQL)

Visualization & User Flows



​UI & UX








​Easy Deployment


Integration with other tools



Free Plan




(open-source, plus paid cloud version)

✓✓ - Best-in-class

- Good enough

- Poor

Detailed Assessment

1. Data Collection/ Instrumentation

Grafana wins. Prometheus supports just metrics, while Grafana agent can be used for the collection & forwarding of traces and logs as well.

The Prom agent introduced in 2021 was inspired by the Grafana agent and mainly takes the code related to metrics functionality from it.

In summary, the Grafana agent trumps for a few reasons -

  1. Allows you to collect & forward traces and logs as well from multiple data sources.

  2. You can send data to OTel systems as well (not just Prometheus-based ones)

  3. Allows more control over the agent’s components with Grafana’s rich UI debugging capabilities

Prometheus agent is preferred in situations where teams are only focused on metrics data or are in the process of switching between standard Prometheus to prom agent.

See here for a more detailed comparison between the Prometheus and Grafana agents.

2. Data Storage

Prometheus shines with its time-series DB for efficient metrics storage. Grafana now has data storage back-ends across metrics, traces, and logs. Loki for log aggregation and storage, Tempo for distributed traces, and Mimir for metrics.

Within metrics, how do Grafana Mimir and Prometheus compare?

Firstly, note that Grafana Mimir builds on Prometheus and many pieces of it have Prometheus code so there is some overlap :) In general, Prometheus is more widely used/ popular. However, Mimir is a more modern metrics solution that addresses many of the challenges with Prometheus like multi-tenancy, longer retention, and faster queries (see here for a more detailed comparison), so Mimir is more robust.

Grana Mimir wins here on pure design and features but it's still a year old. If you want a more tested solution, Prometheus is preferable.

They’re compatible with each other, so if you have a Prometheus agent, you could just set it to send data to a Mimir cluster easily.

3. Scalability

Prometheus adopts a pull-based, single-tenant model which, while straightforward, poses challenges as systems grow. To handle vast amounts of data, Prometheus typically requires sharding and federation, adding some complexity.

Grafana Mimir, on the other hand, is built for scalability and high performance. It has a distributed multi-tenant model that allows you to scale horizontally seamlessly and a dedicated long-term storage solution for storing and processing vast amounts of data.

Grafana Mimir wins on scalability.

is 4. Querying

Prometheus's functional query language, PromQL, is both robust and expressive, allowing users to extract intricate details from their metrics. Alerts in Prometheus are defined using the same query language, ensuring precision. Grafana can leverage PromQL as well. In keeping with the theme of both companies building on top of each other, Grafana has also built its own Prometheus query builder, which improves on PromQL. Both perform the same here.

5. Alerting

Prometheus has a separate component called the Prometheus Alert Manager, that allows you to create and manage any alerts based on Prometheus data. It’s widely used, proven, and well-liked. Historically, Grafana alerting was limited to data on the dashboards. However, with Grafana’s evolution into full-stack, Grafana alerting has become more comprehensive.

Grafana Alerting now allows you to define alerts based on any Grafana data (Loki logs, Mimir, Tempo traces). The engine allows you to define alert criteria, evaluation frequency, time duration for evaluation, and composite criteria and also set notification policies like where and to whom the alerts are routed. You could mute alerts for a while, or stop receiving notifications for a specific alert altogether. That said, Prometheus AlertManager still has an edge within metrics as it allows for more complex alerts with complex queries and calculations, with better performance. Grafana Alerting uses a SQL database so performance may not be great. Prometheus wins.

6. Visualization

For data visualization, Grafana is the star. Its dashboards are customizable, intuitive, and designed for a great user experience. Prometheus, on the other hand, has a basic visualization interface. It's functional but lacks the polish and flexibility Grafana offers.

If rich visuals and dashboards are your focus, Grafana is the clear choice.

7. UI & UX

Again, Grafana is a clear winner. Grafana offers a sleek, user-friendly interface, making dashboard creation and navigation a breeze. Prometheus's UI is pretty basic and best avoided. If you're looking purely for functionality and don't want the learning curve that Grafana requires, Prometheus gets the job done.

8. Collaboration and Team Management

With built-in features like user roles, permissions, and team-centric dashboards, Grafana enables easy collaboration.

Prometheus, on the other hand, is not designed for it.

9. Documentation

Both projects have detailed resources. Prometheus distinctly carves a niche with detailed help on the metric collection, including best practices and common pitfalls. Grafana hosts an extensive library of resources spanning tutorials on dashboards, panels, and its expanding list of plugins. While Prometheus's documentation reads like a deep, technical manual, Grafana offers a blend of user guides, tutorials, and community-contributed content.

Both projects are very well-documented and have vibrant communities.

10. Deployment

Prometheus is straightforward to deploy due to its standalone nature with configurations primarily via YAML files. This makes its initial setup somewhat swift. Grafana, conversely, offers a lot of integrations, making it versatile but forcing a steeper initial learning curve. Prometheus speaks the language of simplicity, while Grafana promises adaptability. For teams preferring a plug-and-play approach, Grafana might demand a bit more patience, but its flexibility is often worth the elbow grease.

11. Integrations

Prometheus, with its dedicated exporters, can extract metrics from a wide variety of sources. Grafana, also has a vast array of plugins that support numerous data sources, helping in seamless integration.

This is just a function of whether you’re looking for metrics alone, or also for other observability.

12. Pricing

Both projects are 100% open-source - Prometheus with an Apache v2.0 license, while Grafana has an AGPL license. Prometheus does not have a cloud version. However, several other players offer hosted Prometheus- e.g., Amazon-managed service for Prometheus, Google Cloud-managed service for Prometheus, etc. There are several other independent players as well offering managed Prometheus.

Grafana on the other hand offers its own cloud version which is paid for. It’s a robust, tightly integrated offering that brings the best of the proven Grafana stack and makes it available as a hosted solution,

Better Together?

As we saw above, Grafana and Prometheus build on each other a lot and are happy partners in the open-source observability ecosystem.

The decision is often not really Grafana vs. Prometheus, but how to use Prometheus and Grafana together in the best way possible. 

Grafana and Prometheus in Practice: Typical Combinations & Configurations

In real-world observability scenarios, the flexibility of Prometheus and Grafana allows for a range of configurations, each tailored to suit different requirements. Here's a quick dive into how these tools are commonly set up together for metrics:

Grafana-Prometheus configurations in Monitoring

Within monitoring, companies do Grafana-only, Prometheus-only, or. combination of the two (see image below).

Prometheus and Grafana: Configuration for monitoring only
Prometheus and Grafana configurations for monitoring
  1. Prometheus back-end + Grafana visualization: This setup is quite popular. Companies here use Prometheus servers/ agents with the Prometheus DB and use Grafana to visualize the metrics.

  2. Mimir + Grafana visualization: Increasingly becoming popular. Teams adopting this are looking for cohesion - the same platform doing the back-end and front-end. They deploy Grafaan agents, push data to Mimir, and visualize on Grafana dashboards.

  3. Prometheus server + Prometheus visualization: This is less common. It's typically adopted by teams with specific needs or those that are in the nascent stages of their observability journey. However, as organizations scale and demand more intricate visualizations, they often switch to Grafana for a broader visualization palette.

Grafana-Prometheus configurations in overall Observability stack

1. Prometheus for metrics alone, Grafana for the rest

Prometheus vs Grafana: Configuration for overall observability
Prometheus for metrics, Grafana for everything else

This is where teams use Prometheus for just metrics back-end and Grafana for traces, and logs, with an integrated visualization layer.

This allows for a single-pane-of-glass experience, where the developer sees all observability data on the same dashboard. It's also one of the most commonly preferred configurations. Most teams already have Prometheus setup as their monitoring tool and are used to it, so tend to prefer this model. The native compatibility between Prometheus and Grafana visualization makes this a popular choice.

2. Grafana stack for everything

Grafana stack for entire observability
All-in LGTM Grafana Observability stack

This is the full Grafana observability option, widely known as the LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics).

This is being adopted by much more modern teams who’re either setting up their observability anew, or refreshing their stack, and are looking for less expensive options vs. the commercial players. This offers a tightly integrated experience much like a Datadog or NewRelic, while having the advantages of being open-source and flexible.

What’s Next? AI layer on top

Once you have your basic observability set up, what next? Recent developments in AI are set to dramatically change how we implement observability. Even with a strong observability stack, developers still need to navigate large volumes of data to zero in on incident-specific data that they’re looking for.

There’s a new class of AI solutions (e.g., ZeroK) that solve this - they sit on top of your existing observability stack and use AI to allow you to debug issues more rapidly.

AI Inferencing with Observability - ZeroK
AI Observability layer

When a production incident occurs, these AI observability solutions pull incident-specific data from across Prometheus, Grafana, and the rest of your observability stack, and generate AI inferences on the most probable root causes. This helps drastically reduce MTTR and also offers a unified incident-specific dashboard for troubleshooting. You can sign up for early access here.


We looked at a comprehensive assessment of Prometheus vs. Grafana - their offerings, where they overlap and how they differ, how they perform across different dimensions, and how they're often used together. They're both robust offerings within their own categories and liberally borrow from each other. Both have contributed significantly to advancing the open-source observability ecosystem.

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