Native Vector Search in SQL Server 2025: Building AI-Ready Applications

SQL Server 2025 extends the data platform with native vector search, enabling AI-powered similarity queries directly inside the database. New vector data types and built-in index structures allow efficient storage and querying of high-dimensional embeddings (e.g., from NLP or image models). With optimized Approximate Nearest Neighbor (ANN) algorithms and vector indexes, queries achieve millisecond-level response times - even across millions of vectors.

We will explore how vector indexes optimize performance by accelerating similarity searches and reducing resource consumption, while also discussing their limitations - such as index build times, storage overhead, and scenarios where approximate results may differ from exact matches.
Beyond the database engine, you will see how SQL Server 2025 integrates with Azure Foundry and Azure OpenAI Service, enabling embeddings generated by text and image models to be ingested directly into SQL via API calls. This allows developers to build end-to-end pipelines where model outputs are stored, indexed, and queried in real time - without external search infrastructure.

Use cases include real-time personalized recommendations, semantic analytics, and anomaly detection for security scenarios, all running on your proven SQL platform. Seamless integration with Azure hybrid scenarios enables vector search to scale across on-premises and cloud environments, while enterprise-grade features such as role-based access control and Always Encrypted ensure secure handling of sensitive embeddings.

By the end of this session, you will understand how to leverage SQL Server 2025’s native vector search, vector indexes, and AI integration to build scalable, secure, and intelligent applications - while being aware of their trade-offs and best practices for real-world deployments.