Stateful ai workloads on kubernetes
Every RAG team hits the same wall six months in. They started with PostgreSQL and pgvector because it was the path of least resistance, the embeddings worked, the retrieval was fine, and nobody had to learn a new system. Then the vector count crossed some invisible threshold — usually around 10M–50M, depending on dimensionality — and recall started sliding, query latency started climbing, and the migration conversation began. The problem: migrating from pgvector to a dedicated vector database while you have live embeddings in production is brutal, and most teams either over-engineer (spinning up a Weaviate cluster for 10K vectors) or under-engineer (running pgvector into the ground at 50M).
This is the decision tree I wish someone had handed me. It's tuned for Kubernetes, June 2026, and every number is from a primary source.