Elasticsearch: The open source platform for high-performance search, context engineering, and AI

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, as well as 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.

Elasticsearch is built for relevance at scale, which is the foundation of context engineering. It brings together vector, keyword, and structured search with analytics, inference, and observability in a single platform. This makes it easy for developers to store, retrieve, and rank structured and unstructured business data with precision, so agents always get the right context.

With Agent Builder, Elasticsearch takes this further by bringing chat, retrieval, tool creation, and orchestration directly into the platform. Developers can build, test, and scale context-driven agents in minutes using their own data, models, and tools, all supported by Elasticsearch relevance, security, and performance.

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.