# Grafana vs Kibana: How to Choose in 2026
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
## Overview
| Feature | Grafana | Kibana |
| ---------------------------- | ---------------- | ---------------- |
| Easy installation/deployment | ✔✔ | ✔ |
| Data processing | ✔✔ | ✔✔ |
| Data querying | ✔✔ | ✔✔ |
| Data source integration | ✔✔ | ✖ |
| UI and UX design | ✔ | ✔✔ |
| Visualization | ✔✔ | ✔✔ |
| Alerting | ✔ | ✔ |
| 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](https://grafana.com/grafana/download), 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](https://www.elastic.co/downloads/kibana). 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 Stack + Grafana/Kibana
While both Grafana and Kibana have improved their alerting capabilities in recent versions, they still lack the comprehensive incident management features that dedicated platforms provide. If you need more robust alerting, on-call scheduling, and incident response than either tool offers, [Better Stack](https://betterstack.com/) delivers a complete solution that works seamlessly with both platforms.
### Superior dashboards and visualizations
While Grafana excels at time-series visualization and Kibana specializes in Elasticsearch data exploration, Better Stack provides an intuitive alternative that unifies metrics from any source. Create powerful dashboards using drag-and-drop builders, SQL, or PromQL without the complexity of multiple tools.
Better Stack's dashboard builder automatically aggregates metrics from your existing data sources, providing ready-to-use templates alongside powerful customization options. Build time series charts, gauges, and stat panels with just a few clicks.
### Superior incident management for visualization tools
Better Stack integrates with both Grafana and Kibana, providing enterprise-grade incident management that transforms how you respond to alerts. Get unlimited phone call and SMS notifications, intelligent on-call scheduling, and native integrations with Slack and MS Teams for faster incident resolution.
Integration is straightforward for both platforms. For Grafana, navigate to Better Stack's integrations page, select Grafana under Infrastructure monitoring, and create a webhook to route alerts. For Kibana, use Better Stack's webhook connector to send Elastic alerts directly to the platform. Both setups take minutes and preserve your existing visualization dashboards while dramatically improving alert handling.
### Flexible on-call scheduling and escalations
Better Stack provides sophisticated on-call management that neither Grafana nor Kibana can match natively. Create rotation schedules, define escalation policies based on alert priority, and integrate with Google Calendar or Outlook for seamless schedule visibility.
During incidents, Better Stack automatically creates comprehensive timelines, merges related alerts to reduce alert fatigue, and enables team collaboration through integrated chat platforms.
### Comprehensive monitoring beyond dashboards
Beyond enhancing Grafana and Kibana alerting, Better Stack provides complete uptime monitoring with checks every 30 seconds. Monitor HTTP endpoints, APIs, SSL certificates, DNS records, and domain expiration across multiple global locations.
### Unified log management
While Kibana focuses on Elasticsearch logs and Grafana requires separate log sources, Better Stack provides centralized log management with live tail, SQL queries, and automated anomaly detection. The eBPF-based collector automatically instruments Kubernetes and Docker clusters without code modifications.
### AI-powered debugging assistance
Better Stack includes an AI SRE that examines logs, metrics, and traces during incidents to identify potential root causes. This intelligent support helps you resolve problems faster than manually searching through Grafana dashboards or Kibana discover views.
### Customer-facing status pages
Unlike Grafana and Kibana which serve internal teams, Better Stack includes customizable status pages for external communication. Automatically update status when incidents occur, notify subscribers via email or SMS, and provide historical uptime transparency.
### Transparent and predictable pricing
Better Stack offers clear pricing with responder licenses starting at $29/month, including unlimited phone and SMS alerts. The free tier provides 10 monitors, 1 status page, plus Slack and email notifications. Since both Grafana and Kibana offer self-hosted options, adding Better Stack delivers professional incident management without replacing your visualization investment. A 60-day money-back guarantee lets you test the platform without risk.
[Sign up for a free account](https://betterstack.com/users/sign-up) and enhance your monitoring today.
## 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:
- [Grafana vs Prometheus](https://betterstack.com/community/comparisons/prometheus-vs-grafana/)
- [ELK stack vs Splunk](https://betterstack.com/community/comparisons/splunk-vs-elastic-stack-elk/)
- [Datadog alternatives](https://betterstack.com/community/comparisons/datadog-log-management-alternatives/)
- [Nagios vs Zabbix vs Prometheus](https://betterstack.com/community/comparisons/nagios-vs-zabbix-vs-prometheus/)