Elastic AI Search

Elastic AI Search brings vector search, semantic retrieval, and AI-powered relevance to the battle-tested Elasticsearch platform used by thousands of organizations worldwide. It supports hybrid search that combines traditional BM25 keyword matching with dense vector embeddings and learned sparse representations, allowing teams to blend lexical precision with semantic understanding for superior result quality. Built-in inference endpoints integrate with embedding models from OpenAI, Hugging Face, and other providers.

Enterprise teams already running the Elastic Stack for logging, observability, or search can add AI-powered search capabilities without migrating to a new platform, preserving existing infrastructure investments and operational expertise. Elastic offers both self-managed and cloud-hosted deployment options, with the self-managed option supporting air-gapped and on-premise environments for regulated industries. Enterprise security features include field-level encryption, role-based access control, audit logging, and cross-cluster replication for disaster recovery.

Elastic AI Search differentiates itself as the only major vector search solution built on a proven, horizontally scalable distributed search engine. While purpose-built vector databases offer similar embedding search capabilities, Elastic provides the full spectrum of search functionality -- full-text search, aggregations, geospatial queries, and vector search -- in a single platform. This makes it the natural choice for organizations that need AI-enhanced search alongside their existing Elasticsearch workloads without introducing a separate vector database into their architecture.