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OpenSearch vs. Elasticsearch: A Comprehensive Comparison

Anastasios Antoniadis

Share on X (Twitter) Share on Facebook Share on Pinterest Share on LinkedInElasticsearch has dominated the search engine landscape for many years, renowned for its powerful full-text search capabilities, scalability, and flexibility. However, the emergence of OpenSearch has introduced a new dynamic into the ecosystem. OpenSearch, a fork of Elasticsearch and Kibana, was created by …

Elasticsearch vs OpenSearch

Elasticsearch has dominated the search engine landscape for many years, renowned for its powerful full-text search capabilities, scalability, and flexibility. However, the emergence of OpenSearch has introduced a new dynamic into the ecosystem. OpenSearch, a fork of Elasticsearch and Kibana, was created by Amazon in response to licensing changes made by Elastic, the company behind Elasticsearch. This article compares OpenSearch and Elasticsearch, covering their history, core features, performance, scalability, security, and use cases to help you understand which solution might best fit your needs.

Background and History


Elasticsearch, released in 2010 by Shay Banon, quickly became a popular open-source search and analytics engine. It’s part of the Elastic Stack (formerly ELK Stack), which also includes Logstash (a data processing pipeline) and Kibana (a visualization tool). Elasticsearch’s rise to prominence can be attributed to its speed, scalability, robust feature set, and active community.

In early 2021, Elastic changed the licensing for new versions of Elasticsearch and Kibana from the Apache License 2.0 to the Server Side Public License (SSPL) and the Elastic License, which are source-available but not open-source licenses in the traditional sense. This change limited the ability of third parties to offer Elasticsearch as a service without collaborating with Elastic.


OpenSearch was launched by Amazon Web Services (AWS) in April 2021 as a community-driven, open-source fork of Elasticsearch 7.10.2 and Kibana 7.10.2, the last versions released under the Apache License 2.0. OpenSearch aims to continue the legacy of Elasticsearch and Kibana under an open-source license, ensuring that the project remains free and open for the community to use, modify, and distribute.

Core Features

Both OpenSearch and Elasticsearch offer similar core features, including:

  • Full-Text Search: Powered by the Lucene library, they provide advanced full-text search capabilities with support for complex queries.
  • Scalability: Designed to scale horizontally across multiple nodes, they can manage and search large volumes of data.
  • Real-Time Operations: They offer near real-time search and analytics capabilities.
  • Data Ingestion and Processing: Both systems allow for the ingestion, processing, and analysis of structured and unstructured data.
  • Visualization and Dashboards: OpenSearch Dashboards and Kibana provide powerful tools for data visualization and management.

Performance and Scalability

Both Elasticsearch and OpenSearch are designed with performance and scalability in mind. They share a common architecture that distributes data across a cluster of nodes, enabling them to handle large volumes of data efficiently. Users of both technologies can expect similar performance levels, assuming the hardware and configurations are comparable.


Elasticsearch initially offered advanced security features such as encryption, role-based access control, and audit logging as part of its commercial X-Pack offering. However, in response to OpenSearch and to better serve the community, Elastic has made these features freely available in the basic (free) tier.

OpenSearch includes security features out of the box, ensuring that users have access to encryption, authentication, authorization, and audit logging without requiring an additional subscription or package.

Community and Ecosystem


Elasticsearch benefits from a large, active community and a mature ecosystem. It has a wealth of plugins, integrations, and client libraries available, supported by Elastic and community contributors.


As a relatively new project, OpenSearch is in the process of building its community and ecosystem. However, being a fork of Elasticsearch, it inherits a strong foundation. Amazon AWS backs OpenSearch, promising continued development and support.

Licensing and Usage

The key difference between the two projects lies in their licensing. Elasticsearch’s move to the SSPL and Elastic License restricts certain types of commercial redistribution and service offerings. In contrast, OpenSearch remains under the more permissive Apache License 2.0, encouraging broader use and contributions from the community.

OpenSearch Pros & Cons


  • Open Source: OpenSearch is fully open-source under the Apache License 2.0, ensuring transparency, community-driven development, and the possibility to modify the software to meet specific needs without facing licensing costs or restrictions.
  • Scalability: Designed to scale horizontally, OpenSearch can handle large volumes of data and supports clustering, making it capable of growing with your data and traffic needs.
  • Real-Time Operations: It enables real-time search and analytics, which is crucial for applications requiring immediate insights from their data.
  • Versatile Search Capabilities: Offers powerful full-text search, structured search, analytics, and supports complex queries, making it suitable for a wide range of applications, from simple website search to complex logging and monitoring solutions.
  • Rich Data Analysis Features: Comes with built-in features for analyzing and visualizing data, including aggregations, metrics, and dashboards, helping users to extract actionable insights from their data.
  • Strong Community Support: Benefits from a vibrant community that contributes plugins, integrations, and improvements. Community forums and resources are valuable for troubleshooting and learning.
  • Compatibility with Elasticsearch APIs: Maintain strong compatibility with Elasticsearch APIs, allowing for easier migration from Elasticsearch and using existing tools and integrations.


  • Complexity in Setup and Management: Despite Docker and orchestration tools simplifying deployment, the setup, tuning, and management of OpenSearch clusters can be complex, especially for large-scale or highly available configurations.
  • Resource Intensive: Can be resource-intensive, particularly for larger datasets and complex queries, requiring significant computing, memory, and storage resources to perform optimally.
  • Learning Curve: The breadth of features and capabilities means there’s a steep learning curve for new users, particularly those unfamiliar with search and analytics platforms.
  • Documentation and Resources: While improving, documentation and third-party resources can sometimes lag behind the software’s development, making it harder for users to find up-to-date information or solutions to specific problems.
  • Migration Challenges: Organizations migrating from Elasticsearch might face challenges due to differences between the two platforms, especially if they rely on features or plugins not fully supported or implemented differently in OpenSearch.
  • Performance Concerns: Some users have reported performance issues, especially in comparison with Elasticsearch, though this can vary greatly depending on the specific use case and deployment configuration.
  • Community vs. Vendor Support: While community support is strong, some organizations may miss the vendor-backed support available for proprietary solutions, although commercial support options are available through third parties.

Elasticsearch Pros & Cons


  • Powerful Search Capabilities: Elasticsearch excels at full-text search, structured search, and analytics, supported by a diverse set of features like scoring, faceting, and nested queries, making it highly versatile for many use cases.
  • Real-Time Data and Analysis: It provides real-time search and analytics capabilities, allowing for immediate insights into the latest data, which is crucial for time-sensitive applications.
  • Scalability and Resilience: Designed to scale horizontally across multiple nodes, it ensures high availability and resilience, capable of handling petabytes of data and thousands of queries per second.
  • Robust Ecosystem: Part of the Elastic Stack, it integrates seamlessly with Logstash and Kibana, offering a powerful set of tools for data ingestion, enrichment, storage, analysis, and visualization.
  • Strong Community and Ecosystem: Benefits from a large and active community, offering a wealth of plugins, integrations, and tools that extend its capabilities. The community also provides significant support through forums and documentation.
  • Restful API: Offers a comprehensive and well-documented RESTful API for managing data and configurations, making it accessible to developers with varied backgrounds.
  • Operational Simplicity: Provides features like automatic node recovery and shard distribution, simplifying operational aspects of managing a distributed system.


  • Complexity in Large-Scale Deployments: While scalable, managing and tuning Elasticsearch for large-scale deployments can become complex, requiring deep understanding and expertise.
  • Resource Intensive: Can be demanding on system resources, especially memory, due to its in-memory indexing and search capabilities. Proper sizing and tuning of hardware are essential for optimal performance.
  • Learning Curve: The breadth of features and capabilities means there’s a significant learning curve, especially for users new to distributed systems or search engines.
  • Security Features in Open Source Version: Basic security features like encryption, role-based access control, and auditing are available in the basic (free) version, but advanced security features require a subscription.
  • Rapid Release Cycle: The fast pace of development and frequent releases can introduce challenges in keeping up with changes, upgrades, and ensuring compatibility with existing systems.
  • Cost of Scaling: While open-source, operational costs can grow with scale, especially in cloud environments where resource usage directly impacts costs. The managed Elasticsearch Service (Elastic Cloud) simplifies some aspects but comes with additional costs.
  • Document Update Performance: Updating documents in Elasticsearch can be less efficient than in some traditional databases, as updates are essentially a delete followed by a re-index operation.

Choosing Between OpenSearch and Elasticsearch

The choice between OpenSearch and Elasticsearch depends on several factors:

  • Licensing Concerns: If open-source licensing is a critical factor for your project, OpenSearch may be the more appealing choice.
  • Feature Set and Ecosystem: Elasticsearch has a more extensive set of features and a mature ecosystem, which might be beneficial for complex use cases.
  • Community and Support: Consider the level of community engagement and support you require. Elasticsearch has a larger community, while AWS backs OpenSearch.
  • Future Developments: Both projects are actively developed, but their roadmaps may diverge over time. Evaluate which project’s vision aligns more closely with your needs.


OpenSearch and Elasticsearch offer powerful search and analytics capabilities, but differences in licensing, community support, and future development paths distinguish them. Your choice will depend on your specific requirements, including licensing preferences, desired features, and the level of community engagement you’re looking for. Both projects have their merits and can serve as the backbone for complex search and analytics applications.

Anastasios Antoniadis
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