Why vector databases are showing up in every AI stack

Retrieval-augmented generation needs a way to find passages similar in meaning to a question. That is what vector search does: it stores embeddings and returns the nearest ones.

Many teams start with a vector extension in the database they already run, and move to a dedicated vector database only when data volume, filtering needs or latency demand it.

What it means for you: learn how embeddings, similarity metrics and approximate nearest-neighbour indexes work – the concepts transfer across every product.