Elastic launches Elasticsearch Vector Database for AI search

Elastic N.V. just rolled out a purpose-built vector database, and it’s aimed squarely at the AI infrastructure layer that every enterprise is scrambling to build. The Elasticsearch Vector Database, announced on September 11, is a serverless product running on Elastic Cloud Serverless that’s designed to handle vector search at massive scale, think hundreds of billions of vectors. What the product actually does Vector databases are the memory layer behind modern AI applications. When a large language model needs to retrieve relevant information before generating a response, a process called retrieval-augmented generation (RAG), it queries a vector database to find semantically similar content. Elastic’s new offering is built specifically for this use case, along with powering AI agents that need to search across large knowledge bases. The product ships with pre-optimized configurations for indexing, embedding, chunking, and hybrid retrieval. In plainer terms: developers don’t have to spend weeks tuning the system before it works well with their AI pipelines. One of the more notable technical features is support for multiple data types, including text, image, and multi-modal vectors, ...