Grafana vs Kibana: How to Choose in 2024

Eric Hu
Updated on January 11, 2024

Grafana and Kibana are two of the most well-known open-source data visualization platforms. However, despite having many similar features, they still have several differences that may lead you to prefer one over the other.

Grafana is a tool commonly used for monitoring and performance metrics visualization. It allows you to compile time series data into charts, graphs, maps, and dashboards. Grafana is also highly customizable and can be integrated with a variety of data sources, including Prometheus, InfluxDB, and Elasticsearch.

Kibana, on the other hand, is an open-source data visualization and exploration tool that is part of the Elastic Stack and is used with Elasticsearch. It comes with a variety of visualizations as well as tools for data searching and filtering.

In this article, we will compare the pros and cons of Grafana and Kibana, so you can discover which one is better suited for your needs. The comparison will be based on the following criteria:

  1. Easy installation/deployment
  2. Data processing
  3. Data querying
  4. Data source integration
  5. UI and UX design
  6. Visualization
  7. Alerting
  8. Team collaboration
  9. Documentation and support
  10. Pricing


Feature Grafana Kibana
Easy installation/deployment ✔✔
Data processing ✔✔ ✔✔
Data querying ✔✔ ✔✔
Data source integration ✔✔
UI and UX design ✔✔
Visualization ✔✔ ✔✔
Team collaboration
Documentation and support ✔✔
Pricing ✔✔

✖ - does not support

✔ - partial support

✔✔ - full support

1. Easy installation/deployment: Grafana

Both Grafana and Kibana offer you the option to self-host on your server.

For Grafana, you need to have:

  • A supported operating system such as Linux, macOS, or Windows.
  • A supported version of Go (currently version 1.13 or later).

Once you have satisfied these prerequisites, you can install Grafana by going to the downloads page, and follow the corresponding installation instruction based on your operating system. The installation process should take less than 10 minutes.


As for Kibana, it requires you to have Elasticsearch installed first and then you can install Kibana from the Kibana downloads page. Just like Grafana, Kibana is also supported on Linux, macOS, and Windows.

Both tools are relatively easy to install and set up, but Kibana requires you to install Elasticsearch first which gives Grafana a slight edge.

2. Data processing: tie

Both Grafana and Kibana come fully-loaded with various data processing and analysis functions. However, they have different primary focus.

Grafana is commonly used in the context of monitoring and observability, where it is used to display data from various sources, such as Prometheus, Elasticsearch, and InfluxDB. Metrics are collected in specific predefined intervals. For example, you can collect various hardware metrics every second and monitor them in real-time using Grafana's highly customizable interface.

Kibana, on the other hand, is part of the Elastic Stack and it is used to process and analyze data stored in Elasticsearch. It is known for its powerful search capabilities and its ability to help users uncover insights from large volumes of data. It also offers a range of visualization options, including pie charts, line graphs, and maps, that can be used to create beautiful and informative dashboards.

It is difficult to say which one is better as it depends on what you are monitoring. If you are looking for a tool to monitor and analyze time-series data, then Grafana may be a good option. If you are working with data that is stored in Elasticsearch and you want to use powerful search and visualization capabilities, then Kibana may be a better fit.

It's also worth noting that Grafana and Kibana can also be used together, as Grafana has the ability to read data from Elasticsearch and display it in dashboards.

3. Data querying: tie

Grafana uses a Domain Specific Language called Grafana DSL to define the metrics and data to be displayed on a dashboard. Grafana DSL is a query language is created based on PromQL (Prometheus Query Language), which is a powerful and expressive language that allows users to select and aggregate data from multiple sources, perform mathematical operations and filter the data to display only the relevant information.

Kibana uses the Elasticsearch Query DSL to define the queries used to search and filter data in Elasticsearch. The Elasticsearch Query DSL is also a powerful query language that allows users to define complex queries to search and filter data. However, the disadvantage of the Elasticsearch Query DSL is that it is only compatible with the ELK stack.

Both data exploration solutions are equally powerful in terms of functionalities, but Grafana is more flexible and not tied to only one stack.

4. Data source integration: Grafana


Grafana supports a wide range of data sources, including popular time-series databases such as Prometheus, InfluxDB, and Elasticsearch, as well as other sources such as SQL databases, CSV files, and cloud-based services like AWS CloudWatch.


On the other hand, Kibana is only able to pull data from Elasticsearch, as it is part of the Elastic/ELK stack. However, note that Elasticsearch itself is compatible with many different platforms.

5. UI and UX design: Kibana


Grafana has a well-designed user interface that is very easy to navigate. On the left are the navigational links, and on the right, you can create dashboards, set up alerts, and configure settings. The latest version of Grafana also comes with a light/dark mode that follows your system setting.


Kibana's user interface is also very user-friendly. It features a simple and easy-to-navigate toolbar, which provides access to all the features and visualizations available. The dashboard view is designed to be customizable, allowing users to easily rearrange and move elements around to create the desired layout.

Overall, the UI and UX design of Grafana and Kibana are both very user-friendly. However, you might find Kibana easier to use as it has more interactive features such as drag and drop, tooltips, and so on.

6. Visualization: tie

Grafana includes many different types of visualizations, including graphs, gauges, and maps. You can customize these visualizations by adjusting their settings and applying different styles and colors. You can also create your custom visualizations using Grafana's plug-in system.

Kibana also offers a wide range of visualization types, such as line graphs, pie charts, heat maps, and so on, which can be used to display data differently. You can use these visualizations to identify trends and patterns in your data and customize them to meet your specific needs.

Both dashboards provide a great number of customization options for data visualization and there is no clear winner for this round.

7. Alerting: tie


Alerting used to be the most significant difference between Grafana and Kibana, but with the latest update of Kibana, now both tools support alerting and incident management.


You may create custom rules that trigger the alert. When an incident happens, a notification will be sent to the endpoint of your choice, such as email, Slack, PagerDuty, custom webhooks, and so on.

8. Team collaboration: tie

Grafana and Kibana are both enterprise-ready platforms offering enterprise-level features such as user management. For example, you can create, edit, and delete users, grant and revoke access to the platform, assign user roles, and create teams for different users.


Grafana provides a range of collaborative features to help teams collaborate on data-driven projects. These features include creating and sharing dashboards, discussing data points in comments, and quickly accessing data from various sources. Additionally, users can collaborate on queries, share insights, and track changes.


Kibana also provides tools for teams to collaborate and share data, visualizations, and dashboards. Teams can also set up alerts and notifications to share information about changes in their data or other important events. Kibana can also be integrated with other tools, such as Slack, to enable real-time communication and collaboration.

9. Documentation and support: Grafana

When it comes to documentation, both tools offer detailed and well-written docs, and they are both fairly easy to navigate.

But for community support, Grafana has a significantly larger community, with 52.9k stars and 1869 contributors on GitHub, while Kibana has 18.1k stars and 817 contributors. So you are more likely to find community support for your problems when using Grafana.

10. Pricing: Grafana

Lastly, let's compare their prices. Both products offer self-host options, which allows you to run the software on your server.


Grafana Labs also offers a Grafana Cloud product with three price tiers: Free, Pro and Advanced. The Pro tier starts at $8 per month, which is for smaller teams. On the other hand, the Advanced tier is usually for large enterprises, and you'll have to contact Grafana Labs for the exact price.


Kibana is part of the Elastic/ELK stack so if you wish to use Kibana, you'll have to purchase the entire stack. The Elastic stack has four pricing tiers, and the cheapest option starts at $95 per month.

The ultimate alerting setup: Better Uptime + Grafana

Grafana is an excellent tool for data visualization and analysis, but its incident management and alerting features are lacklustre compared to dedicated tools. If you are looking for something more intuitive than what Grafana provides, take a look at Better Uptime. It is a hosted monitoring and incident management platform that can monitor your entire infrastructure and alert you promptly if something goes wrong.


Better Uptime allows you to create alerts for possible incidents when setting up a monitor for your application. For example, you can ask Better Uptime to call, send SMS, email, or push notifications to the current on-call person (or the entire team) if an incident happens.


Integrating Better Uptime with Grafana is quite straightforward. On the Integrations page, choose Grafana under Infrastructure monitoring and follow the instructions to create a webhook. The webhook will channel all your Grafana alerts to Better Uptime instead.


Better Uptime also offers on-call schedule and team management functionalities, allowing you to decide which team member should be notified when an incident occurs.


Final thoughts

In conclusion, both Grafana and Kibana are open-source data visualization tools that are very popular in the world of data analytics and monitoring. They are both capable of creating beautiful and informative dashboards for visualizing data in various ways, such as graphs, charts, and maps.

However, they tend to serve different purposes. Grafana is a platform for visualizing and analyzing time series data, while Kibana is a tool for searching, analyzing, and visualizing data stored in Elasticsearch. Grafana is often used for monitoring purposes, such as tracking the performance of servers and applications. Kibana, on the other hand, is used for more ad hoc analysis and exploration of data.

Overall, they are both powerful tools for visualizing data, but the correct choice between the two will depend on your specific needs and the type of data you are working with.

Other useful resources to explore:

Author's avatar
Article by
Eric Hu
Eric is a technical writer with a passion for writing and coding, mainly in Python and PHP. He loves transforming complex technical concepts into accessible content, solidifying understanding while sharing his own perspective. He wishes his content can be of assistance to as many people as possible.
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