Vector Database

A vector database is a storage system optimised for indexing and querying high-dimensional embedding vectors using approximate nearest neighbour search.

What is Vector Database?

Vector databases use indexes such as HNSW, IVF, or product quantisation to find the most similar vectors to a query in sub-linear time. They typically also support metadata filtering, hybrid search combining sparse and dense retrieval, and multi-tenant isolation. Examples include Postgres with the pgvector extension, Elasticsearch, OpenSearch, Weaviate, Qdrant, Pinecone, and Milvus. The choice depends on operational fit, residency, and existing data infrastructure rather than benchmark micro-differences.

How does Vector Database apply to enterprise AI?

Enterprise RAG systems live or die by retrieval quality. Vector database choice has direct implications for EU data residency, encryption at rest, and recovery objectives.