The hard parts of hybrid retrieval, already done, with optimized defaults, third party and native Jina AI models, and managed GPU inference all out of the box. Build fast, scalable AI apps, not infrastructure. Try out vector search for yourself using this self-paced hands-on learning for Search AI. You can start a free cloud trial or try Elastic on your local machine now. Elasticsearch is one of the most widely deployed platforms for vector workloads in the world, powering semantic search , retrieval augmented generation (RAG), and recommendations for companies like GitHub, Docusign, Seismic, and many others.
Today we're announcing Elasticsearch Vector Database , a new serverless offering optimized for vector based applications. You bring your documents and your queries, and we handle the embeddings and index tuning, along with the infrastructure. Plus, we keep it cheap and scalable. For new users, this is the fastest way to get high-quality vector search running. If you already use Elasticsearch, the new offering is vector search on the platform where your data already lives, with no new system to adopt.
Elasticsearch Vector Database supports a range of scenarios, from grounding a large language model (LLM), to giving an AI agent retrieval and memory, to serving hundreds of billions of vectors. Spin up a new project and get started in minutes. Elasticsearch Vector Database is built for anyone building applications using vectors: RAG: Retrieve the right context for your LLM with dense and sparse vector retrieval, or go with hybrid search combining both vector and lexical retrieval. The quality of your generation improves with the quality of your retrieval.
AI agents: Give agents fast, filtered retrieval over documents and conversation memory, with the low latencies that multistep agent loops demand. Semantic search: Match on meaning, not keywords, with one field type and zero pipeline code. Recommendations and similarity: Find nearest neighbors across products, images, or whatever content you have, at scale.