The most widely deployed vector database
Ship in minutes, scale efficiently to hundreds of billions.
Elasticsearch Vector Database handles the hard parts of hybrid retrieval out of the box. Run vector and keyword search across text, images, and multimodal data on a single index, with native and third-party models, managed GPU inference, and production-ready security and scale.
Best experienced on Elastic Cloud Serverless
"We can scale as the business scales and as the data scales with Elastic and maintain that level of reliability and performance."
Logan PashbyPrincipal Engineer, Cypris
The hard parts of retrieval, already done
Best-in-class relevance with hybrid search, out of the box
Run vector and keyword search across text, images, and multimodal data on one index. Bring your own models or use native Jina AI models on managed GPU inference. No embedding pipeline to build or maintain.
Jina models: #1 on the MMTEB leaderboard
- jina-embeddings-v5-text-small leads every multilingual model under 750M parameters and outperforms significantly larger alternatives.
- jina-embeddings-v5-text-nano leads every model under 500M.
Scale to hundreds of billions, without scaling costs
Store and search hundreds of billions of vectors more efficiently with built-in compression and storage optimizations.
- Up to ~32x vector compression with Better Binary Quantization (BBQ)
- Up to 95% less memory and CPU consumption with BBQ
- Up to 63% reduction in storage by excluding vectors from _source
Production-grade performance and predictable cost without the tuning
Elasticsearch ships pre-tuned for vector workloads, with strong out-of-the-box performance under concurrency, automatic scaling for traffic spikes, and deeper configuration when you need it.
Key benefits
Native hybrid search
Combine BM25 keyword search with vector search in a single query for stronger relevance across exact-match and semantic search use cases.
Simplified semantic search
Skip manual configuration. semantic_text automatically handles embeddings, chunking, and index configuration so you can focus on building search experiences, not infrastructure.
Native embeddings, managed for you
Bring your own models or use native Jina AI models on managed GPU inference. Ingest and search text, images, PDFs, video, and multimodal content on a single index without building or maintaining a separate embedding pipeline.
Filtered vector search
Run vector similarity and metadata filters together in one query. Deliver semantically relevant results that also respect real-world constraints like price, category, availability, or permissions.
Native RAG and agent context
Use native vector database capabilities and connectors to power retrieval augmented generation (RAG) and agentic retrieval workflows, from chatbots to multiagent applications. Keep AI outputs grounded in high-quality proprietary data.
Predictable pricing
Costs scale with your workload, so you can grow and scale back without surprises.