OpenSearch began in 2021 as an Apache 2.0-licensed fork of Elasticsearch and Kibana, created to preserve an open-source search and analytics option after Elastic changed the projects’ licensing. It has since developed into a broader community project, now hosted by the Linux Foundation, with documented vector, semantic, and hybrid search capabilities that can support generative AI applications.
Why OpenSearch was created
The OpenSearch Project says it announced OpenSearch in January 2021 as an open-source fork of Elasticsearch and Kibana. Its FAQ identifies Elasticsearch 7.10.2 and Kibana 7.10.2 as the upstream versions. The project describes its purpose as maintaining an Apache License 2.0 option after Elastic changed the licensing of Elasticsearch and Kibana. This is the project’s account of its history; it is not an independent assessment of the licensing dispute. OpenSearch About and OpenSearch FAQ
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OpenSearch 1.0 became generally available in July 2021, marking the project’s first production-ready release. OpenSearch 1.0 announcement
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OpenSearch is a suite rather than only a query engine. The project describes the core OpenSearch search and data store alongside OpenSearch Dashboards, Data Prepper, and plugins. Those plugins extend functions such as security, analytics, observability, and machine learning. The project says its software is released under Apache License 2.0. OpenSearch About and OpenSearch FAQ
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That breadth matters when evaluating it: search and analytics workflows can involve ingesting and preparing data, querying it, and exploring results through dashboards, with additional capabilities supplied by plugins. The exact components and configuration depend on the system being built.
How the project’s governance changed
On September 16, 2024, the Linux Foundation announced the launch of the OpenSearch Software Foundation and said OpenSearch had transitioned from AWS hosting to the Linux Foundation. Linux Foundation announcement
Foundation oversight and technical project governance are separate responsibilities. The Foundation’s Governing Board oversees the Foundation and administers its budget; the Foundation says it does not provide technical oversight of the open-source project. Technical oversight is handled through the project’s Technical Steering Committee, as described in its technical charter. OpenSearch Software Foundation and OpenSearch technical governance charter
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The OpenSearch Project also states an aim of keeping development on a level playing field: “We will not tweak the software so that it runs better for any vendor (including AWS) at the expense of others.” This is the project’s stated development principle, not an independently audited finding. OpenSearch About
What generative AI adds to search
Vector and semantic retrieval
Traditional full-text search matches terms in a query against text in a collection. Vector search instead stores embeddings—numerical representations of data—and retrieves items that are near a query in vector space. This can help find material related in meaning even when it does not use the same exact words. OpenSearch documents vector search and semantic search as part of its machine-learning and AI capabilities. OpenSearch vector search documentation and OpenSearch Machine Learning and AI documentation
Hybrid search and RAG
OpenSearch documentation also describes combining full-text and vector search. A hybrid approach can use lexical matching alongside semantic similarity, with the balance and implementation chosen for the application. In retrieval-augmented generation (RAG), a search system retrieves potentially relevant material that an AI application can provide to a language model as context for a response. OpenSearch can serve as that retrieval layer; it is not itself a large language model, and vector retrieval alone does not guarantee that generated answers are accurate. OpenSearch neural search documentation and OpenSearch Machine Learning and AI documentation
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Embedding models and implementation choices
OpenSearch documentation says embeddings can be generated using machine-learning models deployed to an OpenSearch cluster. The project’s AI overview describes an extensible machine-learning framework, neural search, vector database functionality, and generative AI agent use cases. These are project-described capabilities, not a recommendation of a particular model, hosting arrangement, or RAG architecture. Check documentation for the OpenSearch version you plan to deploy before relying on specific implementation details. OpenSearch Machine Learning and AI documentation
OpenSearch 3.0 and performance claims
OpenSearch announced general availability of version 3.0 on May 6, 2025. The project reported a 9.5x improvement over OpenSearch 1.3 across key query types. That figure is the project’s benchmark comparison, not an independent, general-purpose result or a guarantee for every workload. OpenSearch 3.0 release announcement
How to assess OpenSearch for an AI-search project
There is no universal best search engine for every AI application. Compare OpenSearch against the needs and constraints of the system you are building:
- Deployment and operations: Decide how the service will be hosted and maintained, and account for the operational work required by your chosen setup.
- Retrieval approach: Determine whether lexical search is sufficient or whether semantic vector retrieval or a hybrid method is needed.
- Models and embeddings: Check how embeddings will be produced, where models will run, and how model changes will be managed.
- Workload performance: Measure latency and scaling against your own data, query mix, and traffic. The project’s 3.0 benchmark does not substitute for workload-specific testing.
- License and governance: Review the Apache 2.0 license and assess whether the project’s governance and contribution model fit your requirements.
The sources cited here do not establish a neutral head-to-head comparison with other search engines, so they cannot support a general claim that OpenSearch is faster, cheaper, or better for a particular workload.
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