Search
Build innovative AI search experiences
The industry's most-used vector database, plus out-of-the-box semantic search, advanced relevance and data retrieval, and flexible provisioning — including serverless. All on the Elastic Search AI Platform.
Use Cases
Search optimized for GenAI
Search applications
For developers, by developers
Build search applicationsMillions of developers rely on the speed, scale, and relevance of Elasticsearch for building their most important search functionality.
Customer support search
Deliver outstanding support with search
Learn about customer supportResolve customer issues and improve satisfaction with intuitive, self-service knowledge base search that serves up the right answer in record time.
Workplace search
Tailor employee search to your teams’ needs
Find out about workplace searchBuild one source of truth with federated search. Use the API, native, or connector clients for indexing data sources and content — at infinite scale.
Elasticsearch — the most widely deployed vector database
See why Elastic is the choice for building personalized, infinitely scalable search AI experiences
Add speed, scale, and relevance with the power of search
CAPABILITIES
Level up your Search AI toolkit
Relevance tuning
Personalized search, unparalleled relevance
Explore relevance tuningBuild search that helps users find exactly what they need. Tune relevance and personalize with RAG optimized for GenAI.
Machine learning and vector search
Take search to the next level with machine learning
Do more with ML and vector searchFlexible capabilities for any development team. Implement next-gen search and NLP apps with textual, vector, hybrid, and semantic search.
Provisioning flexibility
Unparalleled performance on an open platform
Go serverlessDiscover a fully managed production‑ready vector database — on Elasticsearch Serverless.
See Elasticsearch in action
Elastic transforms enterprise search for the world’s most innovative companies.
Customer spotlight
Cisco resolves customer issues faster with search AI experiences.
Customer spotlight
Ernst & Young helps clients mine insights from unstructured data with generative AI.
Customer spotlight
Cypris supports research and development breakthroughs using vector search and RAG.
Frequently asked questions
Yes, Elasticsearch and Kibana are open source under the AGPL license. Built on Apache Lucene, we support open-source projects like OpenTelemetry, Logstash, and Beats. This fosters a community of innovation and collaboration, ensuring Elasticsearch continues to evolve in new and exciting ways. The AGPL license reinforces our open-source principles, ensuring security, extensibility, and community-driven progress.
No. Elastic's BM25 textual search algorithm, its scalable vector database, semantic search, and reciprocal rank fusion (RRF) hybrid scoring all come ready to use with Elasticsearch. Elastic even has its own semantic search model, the Elastic Learned Sparse Encoder, that can be used out-of-the-box. Explore Search AI with these interactive hands-on learning modules.
Yes. Elastic is the world's most used, scalable vector database that lets developers create, store, and search vector embeddings. But that's not all. Elasticsearch also contains everything you need to build outstanding search experiences including aggregations, filtering and faceting, auto-complete, multiple retrieval methods, and the flexibility to integrate with your own or third-party transformer models.
You need a search product if you use a large language model because it's a cost and time efficient approach for achieving more accurate results in your generative AI experience. By searching over your domain-specific data, you can minimize hallucinations from the large language model by providing highly relevant search results as additional context and limit the time it takes to fine tune the model. Using retrieval augmented generation (RAG), Elastic lets you query proprietary data to get more accurate, real-time results, requiring fewer compute and storage resources. Elastic also controls search access with its document-level security.
If you're a developer, one of the best places to get technical and practical information about implementing Elastic is through blogs, examples, and tutorials featured in Elasticsearch Labs. This resource is created and maintained by the technologists who work at Elastic for the technologists who use Elastic to help you learn about the latest in generative AI, vector search, and machine learning research.
Elastic's Search AI Lake is optimized for real-time, low-latency applications, making it an ideal architecture for your AI-driven future. It revolutionizes data lakes by offering low-latency querying and the powerful search and AI relevance capabilities of Elasticsearch. Search AI Lake powers a new Elastic Cloud serverless deployment — removing all operational overhead so your teams can start innovating.
Discover everything you can do with Elastic
