PostgreSQL has become the default database for many applications—but AI workloads are introducing new patterns that go beyond traditional transactional design. Today’s apps need to combine relational data with semantic search, vector similarity, and AI-driven experiences. In practice, this often means multiple systems, data movement, and added complexity. In this session, we’ll take a deep dive into Azure HorizonDB, a PostgreSQL-compatible service engineered for AI-driven workloads. You’ll learn how it brings transactional processing, vector search, and AI integration together in a single system—while improving developer productivity through tools like Visual Studio Code and GitHub Copilot. We’ll focus on what this means for real applications: Where PostgreSQL patterns start to show friction when combining relational queries with AI-driven search How advanced vector search with filtering enables efficient hybrid (relational + vector) queries The shift to shared storage and scale-out compute for performance and elasticity You’ll leave with practical patterns you can apply today—without introducing additional data stores or complex pipelines.