1B row vector search in less than a second with Azure SQL Database Hyperscale | Data Exposed
Azure SQL Database Hyperscale delivers vector search across 1 billion rows in under one second
Microsoft's Azure SQL Database Hyperscale now supports native billion-row vector search with sub-second latency, positioning it as a viable alternative to dedicated vector databases for enterprise RAG and semantic search workloads. The capability includes native approximate nearest neighbor indexing, allowing teams to keep vector and relational data co-located. This matters because most enterprise data already lives in SQL, and eliminating the need to replicate it into a separate vector store reduces complexity and cost for AI application builders.