Every RAG tutorial pulls the same move. It walks you through embeddings, chunking, retrieval, and then right at the moment of truth it says "now pick a vector database" and just... moves on. Like that's a coffee-order decision. Like you're choosing a font.
It's not. The database you pick decides how fast your queries come back, how much you bleed in infrastructure costs, and how miserable your life gets the day traffic triples. And here's the thing that took me a while to figure out while researching this: the database you start with is often not the one you ship. Teams migrate, sometimes more than once. Some of those moves are clean wins; some are weeks of pain and a postmortem doc.
So let's try to skip the regret. I spent a long time digging through benchmarks, production write-ups, and the trade-offs people only discover after the fact, and this is the guide I wish I'd had when I started: what each of these five (pgvector, Pinecone, Qdrant, Weaviate, and Milvus) is actually good at, where each quietly falls apart, and how to pick without drowning in a feature matrix.
I'll spoil the ending, because it's the most useful thing here: pick based on the infrastructure you already run, not the benchmark you just read. If you're already on Postgres, the answer is probably pgvector and you can almost stop reading. If you never want to touch infrastructure again, it's Pinecone. Everything past that is fine-tuning.
