Everyone is benchmarking the wrong thing.
The conversations I keep seeing in enterprise AI architecture circles treat vector database selection as a performance optimization problem. Which database has the best recall at k=10? Which has the lowest query latency at a million vectors? Which scales most efficiently to a billion records?
These are real questions. They are also mostly irrelevant to the actual decision most enterprises need to make.
Here is the uncomfortable truth about vector database selection for enterprise RAG deployments: at the scale of most enterprise knowledge bases — tens of millions of vectors, not billions — every serious vector database performs adequately. The performance differences between Pinecone, Weaviate, Qdrant, Milvus, and pgvector at 10 million vectors are not going to be the factor that determines whether your enterprise AI deployment succeeds.
The factors that determine success are almost entirely about operational fit, security architecture, and deployment model. Not benchmark scores.
