Vector search is at the core of modern AI applications, powering recommendation engines, semantic search, and image recognition. Traditionally, implementing vector-based queries required adding a dedicated vector database, introducing complexity and additional infrastructure. But with SQL Server 2025, you can now store, query, and optimize vector embeddings natively - all within your existing environment, running entirely on-premises.

Why does this matter? For organizations needing full control over their data, SQL Server 2025 enables AI-powered search without relying on external cloud services or third-party databases. Whether working with text, images, or other unstructured data, you can leverage vector search while keeping everything inside your on-premises SQL Server environment. Built-in Row-Level Security (RLS) ensures that sensitive vector data remains protected, making it enterprise-ready for governance and compliance.

In this demo-driven session, we'll break down what vectors are, why they’re essential for AI applications, and how SQL Server 2025 simplifies vector search. You'll learn how to store and query vector embeddings efficiently, explore real-world scenarios like AI-powered recommendations and intelligent search, and discover best practices for indexing and optimizing performance.

If you're looking for a way to integrate AI-powered search within your on-premises SQL Server - without external infrastructure - this is a session you won’t want to miss!