Storage and nearest-neighbour search over embeddings — the retrieval half of most useful AI features.
Semantic search and retrieval over a corpus large enough that scanning it is not an option. Hybrid retrieval — semantic and keyword together — is usually what actually produces good results, so whatever holds the vectors has to be able to do both.
Adding a new database for it by default. Postgres with pgvector handles a great deal more than teams expect, and it is one system rather than two. A separate store has to earn itself against that, and the argument has to be a measurement rather than an architecture diagram.
Retrieval quality measured with a real query set, and a documented path for re-embedding when the model changes. Vectors kept next to the data they describe wherever we can, so there is one system to back up, secure and reason about instead of two that can disagree.
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Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.
