Choosing an embedding model

Similar meaning, nearby vectors

An embedding maps text to a vector so that semantically related passages land close together. That is what lets a query about "cutting cloud spend" match a document about "reducing infrastructure costs" with no shared keywords.

Larger vectors capture more nuance and cost more to store and search. The honest way to choose is to evaluate a few candidates on your own queries, because leaderboard rankings rarely survive contact with a specific domain.

Switching models later means re-embedding everything, and vectors from different models are not comparable. Treat the choice as a migration, not a config change.