Vector Databases Explained for Non-Engineers

A vector database stores information as embeddings, numerical representations of meaning, rather than exact keywords, which lets a search for one phrase match a document using entirely different wording that means the same thing. Choosing one with the right balance of speed, cost and scale is often the first technical decision in AI search projects.

A vector database stores information as numerical representations of meaning, called embeddings, rather than as exact keywords.

This lets a search for "cancel my order" also match a document that says "how to end a subscription," even though the wording is completely different, because the two phrases are close in meaning.

Most of the AI search and chatbot projects we build rely on a vector database behind the scenes, and choosing one with the right balance of speed, cost and scale is one of the first technical decisions we make.

Under the hood, each piece of text is converted into a list of numbers, its embedding, positioned in a high-dimensional space such that texts with similar meaning end up close together. A search query is converted the same way, and the database finds the nearest neighbours to that point.

A common pitfall for non-engineers evaluating this technology is assuming a vector database replaces a regular database entirely; in practice most systems use a vector database alongside a traditional one, with the vector store handling semantic search and the traditional database handling structured records and transactions.

Chunking, how documents are split before being converted into embeddings, matters more than most teams expect. Chunks that are too large dilute meaning and return imprecise matches, while chunks that are too small lose important context.

We evaluate vector database options against a client's actual data volume and latency requirements rather than defaulting to whichever option is most talked about, since the right choice varies significantly with scale.