Elastic Introduces Serverless Vector Database: Ship in Minutes, Scale Affordably to Hundreds of Billions of Vectors
Optimized defaults for fast, best-in-class vector search out of the box
SAN FRANCISCO, September 11, 2026--Elastic (NYSE: ESTC) today announced Elasticsearch Vector Database, a new serverless offering purpose-built for large-scale vector search and AI applications. Elasticsearch is already one of the most widely used platforms for vector workloads worldwide and now developers get an optimized database with expert-tuned defaults to build high-quality vector search applications quickly. Developers bring their documents and queries, and Elastic handles the embeddings, tuning, and infrastructure underneath.
Building a vector-based application today means stitching together multiple parts of the retrieval pipeline: chunking documents, setting up and hosting embedding and reranking models, configuring indexes, storing vectors efficiently, wiring query-time embeddings and rerankers, and retrieving documents behind the matches. Each step adds operational overhead as data volumes grow, with most pure-play vector databases adding unpredictable pricing on top.
Elasticsearch Vector Database handles all of this automatically without the runaway costs:
The right defaults, already set: Expert-tuned, production-grade defaults determine how vectors are stored, indexed and merged, with optimized instance types built for vector workloads. Developers don't need weeks of manual tuning to get fast vector search working. A single field type handles indexing, embeddings, and chunking, so users get semantic search without building an embedding pipeline. Hybrid search is built in, allowing developers to combine full-text and vector retrieval in one query.
High-quality relevance, out of the box: Vector and keyword search run across text, image and multi-modal vectors on one index. Developers can use their own models or Jina AI embedding and reranking models on managed GPUs through the Elastic Inference Service, with no embedding pipeline or model servers to operate, to achieve best-in-class relevance.
Scale to hundreds of billions of vectors with predictable costs: Elasticsearch Vector Database combines optimized instance types and automatic quantization through Elastic's Better Binary Quantization, which shrinks vector memory by up to 32x while keeping search fast and recall high. Pricing is based on data and search capacity, with no opaque compute units and no charges for background operations.
Together, these optimizations help developers ship applications faster while keeping costs low.