Grafana vs Kibana – What’s the Difference ? (Pros and Cons)

Grafana vs Kibana – What’s the Difference ? (Pros and Cons). Kibana is the data visualisation dashboard software available to source for Elasticsearch, whose free and open source successor in OpenSearch. On the other hand, Grafana is a cross platform open source web application for analytics and interactive visualisation. When linked to supported data sources, it makes charts, graphs and alerts available online.

In this blog we introduce Kibana and Grafana application. We learn their pros and cons perform comparison of  differences between them.

So let’s get started with Grafana vs Kibana – What’s the Difference ?

What is Grafana?

Firstly, Grafana is a popular open source analysis and visualization tool. Mainly, it focuses on visualizing metrics for time series databases such as InfluxDB, OpenTSDB, and Prometheus. Also is managed by Grafana Labs, Grafana’s open source parent company.

End users are able to create complex dashboards by combining multiple data sources. A short workflow for creating a dashboard in Grafana includes the following steps:

  • Install Grafana – To be installed on many different operating systems.
  • Add a data source – Provides a drop down list of common data sources such as Prometheus, Graphite, InfluxDB, etc.
  • Create dashboards – After setting up the data source, you use Grafana views to explore and create queries that track the metrics you want to track.

Why Grafana?

Dashboards that anyone can use

Not only do Grafana gives you dashboards to give you deep meaning to data gathered from numerous sources, but you also share dashboards you create with other team members to explore the data together.

Additionally, Grafana enables anyone to create and share dynamic dashboards that promote collaboration and transparency.

Flexibility and versatility

What is more with Grafana, it transforms all your data into flexible and versatile dashboards. Unlike other tools, Grafana lets you create dashboards just for you and your team. Also customize the panel with advanced query and transformation capabilities to create really useful visualizations.

Data everyone can see

Built on the principle that data should be available to everyone in the organization, not just one operations person. Additionally by democratizing data, Grafana helps disrupt data warehouses and strengthen teams by creating a culture where data is easy to use and available to those who need it.

Unify your data, not your database

There is no need to load data into server storage or vendor databases. Instead, Grafana takes a unique approach to providing a one stop shop, bringing your existing data with you wherever you are. Moreover, Grafana allows you to import existing data from your Kubernetes cluster, Raspberry Pi, various cloud services or Google Sheets and visualize it in a single dashboard as you wish.

Key Features of Grafana


Setting up a single dashboard with a few clicks, drag-and-drop is easy enough, but some users would like to be able to extend it even more easily. So, Grafana has a provisioning feature to automate setup with scripts. You can write anything in Grafana. 

For example, if you want to create a new Kubernetes cluster, Grafana automatically supports scenarios where the correct servers, IP addresses, and data sources are already configured and blocked. It is also a way to manage multiple dashboards.

Custom plugins

What is more, Grafana features with plugins that provide additional tools, visualizations, and more. Popular examples are the Worldmap panel (overlays data on a map), Zabbix (integrates with Zabbix metrics), and Influx admin panel (allows you to create databases or add users).

These are just a few examples, but there is many more. By writing some code Grafana renders anything that generates timestamps. Additionally, Grafana Enterprise customers have access to additional plugins that simplify integration with Datadog, New Relic, Splunk and more.

SQL data sources

With built in support for SQL that helps graphically display any data that can be stored in a SQL database. Advanced users do a lot of cool things with SQL data sources, including creating business dashboards.


Supports LDAP, OA and other authentication styles and maps users to organizations. Grafana Enterprise also allows you to map users to teams. So, if your organization uses its own authentication system, Grafana allows you to map teams from your internal system to teams in Grafana. This gives your team members access to automatically assigned Grafana dashboard.

Pros and cons of Grafana


  • Ability to create visually appealing and effective dashboards that help operations teams understand what is happening on the monitored platforms.
  • Great tool that allows users to query and display data by providing connections to many types of data sources (sql, postgreSQL). It also provides users with a dashboard where they  find all the visualizations they have created, such as graphs, tables, and more.
  • When a threshold is violated, an alert is sent to the collaboration channel.
  • Very useful for visualizing metrics coming from known data sources.
  • Easily integrate to Prometheus and Graphite data sources.


  • Best in terms of visualization, but it doesn’t support log analytics and otherwise would break the business of all other visualization tools.
  • Limited on the reporting type supported, which is important for managerial level officers who want reports that are either general or specific.
  • Send alerts and notifications to collaboration channels.
  • The highly customizable properties of Grafana make it challenging and time consuming at the beginning.

Up next with Grafana vs Kibana – What’s the Difference ? article, we introduce Kibana.

What is Kibana?

Next application is an open and free application that sits at the very tip of the Stack. Additionally, it allows you to visualise and search for index data. It is mainly known as a Stack charting tool. In addition, Kibana also acts as a user interface for monitoring, managing and securing the cluster. Moreover, developed in 2013 as part of the Elasticsearch community, Kibana has evolved into a window of the Elastic Stack itself, providing a portal for users and businesses.

What is Kibana used for?

Interestingly, the tight integration with Elasticsearch and the larger Elastic stack is ideal for supporting:

A). Search, view, and visualize indexed data in Elasticsearch and analyze data by creating bar charts, pie charts, tables, histograms, and maps. Dashboard views combine these visual elements and then pass through the browser to provide a real time analytical view of large amounts of data, supporting use cases such as:

B). Monitoring, managing, and securing an Elastic Stack instance via web interface.

C). Centralized access to embedded solutions built on the Elastic Stack for surveillance, security, and enterprise search applications.

Features of Kibana


There are also many plugins that allow you to add new visualizations or user interfaces from community driven plugins. For example, there are plugins for 3D charts and 3D plots.

Analysis and data exploration

Provides a search tab where users explores and analyses data. This is useful for doing ad hoc analysis of data when a specific answer is needed. You quickly create views from your data and optionally create dashboards.

Search and query capabilities

Kibana allows users to search Elasticsearch indexed data through intuitive free text and field search. Kibana uses KQL as the default query language for users to search.


Besides, Kibana allows users to search Elasticsearch indexed data through intuitive free text and field search. 


When your visualizations are ready, you place them all on one board called a dashboard. Looking at the different perspectives of the data together gives an overall clear picture of exactly what is going on.

Pros and cons of Kibana


  • Contains open source browser based visualization tool mainly used to analyse large volume of logs in the form of line graph, bar graph, pie charts, heat maps etc.
  • Timelion visualization in Kibana helps to compare data backwards to understand the performance better.
  • Simple and easy for beginners to understand.
  • Real time observability.


  • Learning curve for initial install can be steep for a production environment.
  • You tend to face issues when you want to upgrade from older version to a new one.
  • Adding of plugins to Kibana can be very tedious, if there is version mismatch.

We have arrived to the main part of this article Grafana vs Kibana – What’s the Difference ? 

Grafana vs Kibana Comparison

Source Image:

Data visualization helps teams monitor their environment, identify patterns, and take action when unusual behaviour is identified. For diagnosis and subsequent root cause analysis, data visualization provides the visibility needed to understand what was happening at any given point in time. Additionally, this is two popular open source tools help users visualize and understand trends in vast amounts of log data.

And here are some notable differences:


Grafana users use the so called query editor to run queries. Each data source has its own query editor specific to the data source. That is, the syntax used depends on the data source. For example, Graphite queries are different from Prometheus queries.

Log querying and searching is one of Kibana’s most powerful features. Also you can use Lucene syntax, the Elasticsearch Query DSL to retrieve data stored in your Elasticsearch index and display the results in chronological order in the main log pane. Lucene is a very powerful query language, but it is not intuitive and requires a bit of a learning curve.

Dashboard and Visualization

It supports graphs, single stats, tables, heat maps, free text panel types, and more. Certainly, it can be customized with many data types. Grafana is built for cross platform visualization and allows you to create dashboards by integrating data from multiple sources. Each panel in a Grafana dashboard corresponds to a specific data source, but you create rich dashboards by combining panels from different data sources.

Provides a variety of visualization types, allowing you to create pie charts, line charts, data tables, single metric visualizations, geographic maps, and more. Beyond the basics, Kibana also provides visualizations for your next analysis:


Comes with Grafana is the alert user interface for creating and managing alerts. Alert rules in the Grafana dashboard allow you to set evaluation criteria for determining whether an alert should be fired. It also provides the ability to configure alert rules with role based access control.

Kibana doesn’t handle warnings directly. Configured in Elasticsearch using Data Watcher. Observers are Elasticsearch features that use data queries to create actions based on conditions that are evaluated at regular intervals, and take actions based on the results. Currently the API is the only way to set the clock.

Data sources

There are significant differences why Grafana has better features overall. 

Mainly, it supports multiple data sources like Prometheus, InfluxDB, OpenTSDB, etc. Elasticsearch is also supported as a data source. Focused on visualizing metrics from time series databases, Grafana officially supports multiple data sources. Here are a few popular ones:

  • Graphite.
  • MySQL.
  • AWS Cloudwatch.
  • Elasticsearch.
  • Prometheus.
  • Tempo.
  • Loki.
  • InfluxDB.

On the other hand, Kibana is designed to work only with Elasticsearch and does not support other types of data sources. To infer data from other sources, it needs to be sent to the ELK stack (via Filebeat or Metricbeat, Logstash, Elasticsearch) to be applied to Kibana.

Team collaboration

Collaboration with Grafana provides a range of 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 collaborate on queries, share insights, and track changes.

Teams also set up alerts and notifications to share information about data changes or other important events. Kibana also integrates with other tools like Slack to enable real time communication and collaboration.


Labs offers Grafana Cloud products at three price points: Free, Pro, and Advanced. Pro tier starts at $8 per month for small teams. On the other hand, the advanced tier is generally reserved for large enterprises and you should contact Grafana Labs for exact pricing.

Kibana is part of the Elastic/ELK stack, so you need to purchase the full stack to use Kibana. There are four price points for the Elastic Stack, with the cheapest option starting at $95/month.

Setup, installation and configuration

Grafana is configured using .ini files that are relatively easy to handle compared to Kibana’s syntax-sensitive YAML configuration files. Grafana also allows you to override configuration options using environment variables.

Kibana supports a wider set of installation options for each operating system, but there is usually no significant difference here. Because Kibana is used on top of Elasticsearch, it requires a connection to an Elasticsearch instance.

Thank you for reading Grafana vs Kibana – What’s the Difference ? (Pros and Cons). We shall conclude the article now.

Grafana vs Kibana – What’s the Difference ? Conclusion

So, whilst Grafana is a platform for visualizing and analysing time series data, Kibana is a tool for searching, analysing and visualizing data stored in Elasticsearch. Grafana is often used for monitoring purposes such as tracking server and application performance. Kibana, on the other hand, is used for more ad hoc analysis and data mining.

All in all, both are powerful data visualization tools, but the right choice between the two will depend on your specific needs and the type of data you’re working with.

Thank’s for your time.

Avatar for Kamil Wisniowski
Kamil Wisniowski

I love technology. I have been working with Cloud and Security technology for 5 years. I love writing about new IT tools.

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