Your Vector Database Decision Is Simpler Than You Think

Every week someone asks which vector database they should use. The answer is almost always "it depends on three things," and none of them are throughput benchmarks. I run semantic search in production on a single VPS. Over a thousand items indexed, embeddings generated on the same machine, queries return in under a second. But that setup only works because of the constraints I'm operating in. Change the constraints and the answer changes completely. Here's how I think about it. The overchoice problem There are dozens of vector databases now. Every one of them publishes benchmarks showing millions of vectors queried in milliseconds. That's great if you're building a search engine for the entire internet. Most of us aren't. The benchmarks test throughput at scale. What they don't test is: can this thing run on the same box as your application without eating all the memory? Can you set it up in ten minutes? Does it need a cluster? Those are the questions that actually matter when you're p