Vector Databases Explained: When You Need One and When You Don't

Vector databases power AI search and retrieval-augmented generation (RAG) by storing text, images, or audio as numerical embeddings that represent meaning, allowing systems to find semantically similar content rather than relying on exact keyword matches. When a user submits a query, it is converted into a vector and compared against stored vectors using approximate nearest neighbor algorithms to quickly surface the most relevant results. For most applications handling up to millions of vectors, the pgvector extension for PostgreSQL offers a practical alternative to dedicated vector databases, enabling similarity search within an existing relational setup without adding a separate service. Specialized vector stores become necessary only at very large scale, under high query throughput, or when advanced hybrid search features are required. Regardless of infrastructure choice, retrieval quality depends more on selecting the right embedding model, combining vector and keyword search, and re-ranking results than on the choice of database engine.