What Is a Vector Database?

A Vector Database is a database designed to store and search data as embeddings, numerical representations that capture meaning, enabling fast similarity and semantic search rather than exact keyword matching.

Traditional databases answer precise questions well but struggle with fuzzy, meaning based ones, finding documents about a topic when the exact words used aren't present in the query. Vector databases have become core infrastructure for exactly that problem, and specifically for AI applications, since retrieval augmented generation, AI agent memory, and semantic search all depend on a vector database to find genuinely relevant information quickly at scale.

We use vector databases as the retrieval layer underneath RAG systems, AI chatbots, and semantic search implementations, choosing between managed and self hosted options based on a client's scale, latency requirements, and existing cloud environment. This is typically paired with a traditional relational database rather than replacing it, since vector search answers semantic questions well but transactional data, permissions, and structured records still belong in a conventional database, so most production systems we build use both together rather than choosing one over the other.