They do not.
I see this confusion often when discussing RAG systems and semantic search, so here is a simple way to separate the two.
A vector index is a data structure.
Its job is narrow.
Given a vector, find the most similar vectors efficiently.
It stores embeddings and uses algorithms like HNSW or IVF to speed up similarity search.
That is all it does.
No metadata handling.
No persistence guarantees.
No APIs.
No lifecycle management.
A vector index is an algorithmic component, not a system.
A vector database is a system.
It stores vectors along with metadata.
It supports insert, update, delete, and query operations.
It handles persistence, scaling, and reliability.
It exposes stable APIs for applications.
Internally, a vector database uses one or more vector indexes to perform similarity search.
Here is the clean mental model.
A vector index answers.
How do I find similar vectors quickly?
A vector database answers.
How do I store and query vectors in a real application?
The takeaway.
Indexes solve search.
Databases solve systems.