EDB Summer Academy

AI applications need a database that can handle more than rows and columns. Vectors, embeddings, semantic search, and real-time inference pipelines are now first-class database concepts. This session explores how Postgres has evolved to support AI workloads, covering pgvector and similarity search and how to structure a GenAI pipeline that keeps your data where it belongs.

We cover the architecture decisions that matter most, from data sovereignty and latency trade-offs to the operational challenges of running AI workloads alongside transactional ones. Leave with a clear picture of where Postgres fits in the modern AI stack.