A vector database stores embeddings and lets you query for the nearest neighbors of a given vector very quickly — millions of vectors in milliseconds. The popular standalone options are Pinecone, Weaviate, Qdrant, and Milvus. Postgres with the pgvector extension works fine for under a few million vectors and saves you a service.
The thing nobody tells you: for most internal use cases, you don't need a vector database. SQLite with a tiny vector extension or even a Python list of vectors can serve a 10,000-document corpus on a laptop. Reach for a vector database when you have millions of vectors, multi-tenant isolation, or hard latency requirements.
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