Indexing Strategies for Vector Search in SQL Server

Vector search is rapidly becoming a core capability for AI-driven applications, enabling semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG). While many teams immediately turn to specialized vector databases, SQL Server is increasingly used as a secure, governed, enterprise-grade vector store—often alongside existing relational workloads.

This session dives deep into indexing strategies for vector search in SQL Server, focusing on how to design, store, index, and query vector embeddings efficiently using relational database principles. We will explore how vector data behaves fundamentally differently from traditional relational data, why classical B-tree indexing fails for similarity search, and which indexing patterns actually work in SQL Server today.

This session is designed for database professionals, data engineers, and solution architects who want to build AI-enabled systems without abandoning their existing SQL Server investments.

1. Understand why traditional SQL indexing fails for vector similarity search and how vector indexing fundamentally differs from relational indexing. 2. Design and implement effective vector indexing strategies in SQL Server, including hybrid search patterns that combine embeddings with structured filters. 3. Evaluate performance trade-offs and architectural limits, enabling informed decisions about when to use SQL Server for vector search and when to introduce specialized vector stores.