Elasticsearch OpenSearch Dashboards vs Kibana: A Comprehensive Comparison

By Opster Team

Updated: Feb 21, 2024

| 3 min read


As data-driven decision-making becomes increasingly important, organizations need powerful visualization tools to analyze and interpret their data. OpenSearch Dashboards and Kibana are two popular options for visualizing and exploring data stored in OpenSearch or Elasticsearch clusters.

In this article, we will compare OpenSearch Dashboards and Kibana, highlighting their similarities, differences, and use cases to help you make an informed decision on which tool to use. If you want to learn about OpenSearch observability visualizations: how to use notebooks and operational panels, check out this guide. You should also take a look at this guide, which contains a detailed explanation on OpenSearch dashboards.

Similarities Between OpenSearch Dashboards and Kibana

1. Data Visualization and Exploration

Both OpenSearch Dashboards and Kibana provide a wide range of visualization options, including bar charts, pie charts, line charts, heatmaps, and more. These visualizations can be combined into interactive dashboards, allowing users to explore and analyze their data in real-time.

2. Querying and Filtering

Both tools support querying and filtering data using the Lucene query syntax or the more advanced Query DSL and SQL. This allows users to narrow down their data sets and focus on specific subsets of data for analysis. 

In addition, Kibana supports the Event Query Language (EQL) for searching event-based time series data and the Kibana Query Language (KQL) that allows to build more complex queries than the Lucene query language. Kibana 8.11 introduced the new and powerful Elasticsearch Query Language (ES|QL), which is a piped query language in the same vein as the OpenSearch Piped Processing Language (PPL), but goes a few steps further by enabling search-time enrichments (aka JOINs) and support for geo_point.

3. Integration with Elasticsearch and OpenSearch

OpenSearch Dashboards can be used with OpenSearch clusters and Kibana can be used with Elasticsearch clusters, providing seamless integration with these popular search and analytics engines.

Differences Between OpenSearch Dashboards and Kibana

1. Licensing and Project Origin

OpenSearch Dashboards is a community-driven project forked from Kibana and is distributed under the Apache License 2.0, which is an open-source license. Kibana is developed by Elastic and since release 7.11 is distributed under the Elastic License and the Server Side Public License (SSPL), which gives users the choice of which license to apply depending on their use case. It is worth noting that both licenses allow users to contribute back. This difference in licensing can be a deciding factor for organizations that prioritize open-source software.

2. Features and Plugins

While both tools offer similar core functionality, there are some differences in the features and plugins available for each. Kibana has a more extensive ecosystem of plugins and integrations, including some that are exclusive to Elastic’s commercial offerings, such as enterprise search, machine learning, observability and security features. On the other hand, OpenSearch Dashboards focuses on providing a fully open-source experience and is actively working on enhancing its capabilities and developing new features and plugins to catch up with Kibana, as it is still lagging in specific areas such as security and observability monitoring.

3. Community and Support

As Kibana is developed by Elastic, it benefits from the backing of a large commercial organization with dedicated support and resources. This can be an advantage for organizations that require enterprise-level support and services. In contrast, OpenSearch Dashboards is a community-driven project, which means that support and resources may be more limited, even though the project is heavily backed by AWS. However, the OpenSearch community is growing rapidly, and there is a strong focus on collaboration and knowledge sharing.

If we look at some numbers, Kibana approaches 20k stars and can count on almost 1000 active contributors, while OpenSearch Dashboards has 1500 stars and a little over 100 contributors. In terms of activities, the Kibana repository sees more than 1000 monthly commits as well as more than 1000 merged PRs and closed issues during the same period, while the number for OpenSearch Dashboards are more towards 50 commits and 50 merged PRs and closed issues per month.

4. Future Development

As OpenSearch Dashboards is a fork of Kibana, its initial feature set is largely similar to Kibana. However, as the projects diverge, the future development of each tool may lead to differences in functionality and capabilities. Organizations should consider their long-term needs and the direction of each project when choosing between OpenSearch Dashboards and Kibana.

Choosing Between OpenSearch Dashboards and Kibana

When deciding between OpenSearch Dashboards and Kibana, organizations should consider the following factors:

1. Licensing: Evaluate whether open-source licensing is a priority.

2. Features and Plugins: Evaluate the specific features and plugins required for your use case. Kibana may offer more extensive options, particularly for Elastic’s commercial offerings, while OpenSearch Dashboards focuses on providing a fully open-source experience.

3. Community and Support: Consider the level of support and resources required. Kibana, backed by Elastic, may offer more comprehensive support, while OpenSearch Dashboards relies on a growing but more limited community-driven support system.

4. Future Development: Keep in mind the potential divergence in functionality and capabilities as the projects continue to develop independently.


Both OpenSearch Dashboards and Kibana are powerful tools for visualizing and exploring data stored in Elasticsearch or OpenSearch clusters. While they share many similarities, there are key differences in licensing, features, community support, and future development that organizations should consider when choosing between the two. By evaluating these factors and aligning them with your organization’s priorities and requirements, you can make an informed decision on the best tool for your data visualization needs.

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