Context engineering: Elasticsearch vs. standalone vector databases

Deliver accurate context for agents by unifying keyword and semantic retrieval across diverse data types, and tune relevance with native reranking models in a single relevance engine without fragmented pipelines.

Keep vector and hybrid search performant at scale with flexible controls to balance cost, latency, and recall using HNSW, DiskBBQ, and quantization, alongside GPU-accelerated indexing, even as you scale to billions.

Chat with your data instantly with a built-in agent, or build your own agents using natural language. Add custom tools and workflows at ease and run everything with native observability.

Secure your AI agents with fine-grained and custom role-based access, document-level security, encryption, and auditing, so they only access and act on authorized data.