What Is a Vector Database?
A vector database stores data as numerical representations called embeddings — long lists of numbers that capture the meaning or characteristics of a piece of text, image, or other content — and is optimized to quickly find embeddings that are mathematically similar to a given query. This similarity search is the core operation that makes techniques like RAG practical at scale.
How It Differs from a Traditional Database
A traditional (relational) database is built around exact matches and structured queries — find the row where the ID equals X. A vector database is built around approximate similarity — find the entries whose meaning is closest to this query, even if no exact keyword matches. This makes it well-suited to searching by concept or meaning rather than by literal text match.
Why AI Applications Need One
Any application that needs to search a large body of content by meaning rather than exact keyword — retrieving relevant documents for RAG, powering semantic search, or finding similar images — needs fast similarity search across potentially millions of embeddings, which is exactly what a vector database is built to do efficiently.
Common Options
Vector database options range from dedicated standalone databases built specifically for this purpose to vector search capabilities added to existing databases and search engines. The right choice generally depends on your existing infrastructure, expected scale, and whether you need a standalone tool or are adding vector search to a system you already use.
Related Pages
Frequently Asked
Do I need a vector database to use AI in my product?
Only if your use case involves searching or retrieving content by semantic similarity, such as RAG or semantic search; simpler AI integrations may not need one at all.
Is a vector database the same as an embedding model?
No — an embedding model generates the numerical representations (embeddings); the vector database stores and searches across them. You typically need both, working together.
Can a traditional database do what a vector database does?
Some traditional databases now offer vector search extensions, blurring the line, but dedicated vector databases are generally optimized for this specific task at larger scale.
How does this relate to RAG?
RAG's retrieval step relies on a vector database to quickly find the most relevant document chunks for a given query before passing them to the model.