Ship production vector search in minutes: Elasticsearch Vector Database workshop

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 indices, 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, with expert-tuned defaults to build high-quality vector search applications quickly, at lower costs.

Join us for a deep-dive of Elasticsearch Vector Database, a new serverless offering purpose-built for vector based applications. You bring your documents and queries, and Elastic handles the embeddings, tuning, and infrastructure underneath.

This session is built for developers, architects, product managers, and data scientists who want production vector search without stitching together a vector database, embedding pipeline, and reranking service.

Highlights

• See how expert-tuned defaults handle vector storage, indexing, and merging on optimized instance types, so you skip the weeks of manual tuning production search usually takes. Opt in to auto-calibration to tune quantization to your data as it changes.
• Run semantic search with no embedding pipeline, using semantic_text, a single field type that handles indexing, embeddings, and chunking for you. 
• Build hybrid search in one query, combining full-text and vector retrieval across text, image, and multimodal vectors on one index.
• Get high-quality relevance out of the box with your own models or Jina AI embedding and reranking models on managed GPUs through the Elastic Inference Service, with no model servers to operate.
• Learn how vectors stay compact and affordable at scale, with Better Binary Quantization shrinking memory up to 32x and DiskBBQ keeping memory low as data grows.
• Get a clear path from RAG to agentic retrieval, covering RBAC, citations, and multi-hop agent patterns using Elastic Agent Builder.

Additional resources

• Meet Elasticsearch Vector Database.
• Start on Elastic Cloud Serverless.
• See how Jina AI can supercharge your semantic search.
• Build with Elasticsearch, the platform for your AI ecosystem.