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Embeddings

Text-to-vector for search and RAG. The invisible workhorse behind semantic search, RAG and recommendations. Reach for an model whenever retrieval comes before generation. Pricing is (there is no generated output to bill), and changing model later means re-indexing everything you have stored.

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What to look for

What to look for in Embeddings

  1. 01

    Input-only pricing

    Embeddings bill on input alone: price per 1M tokens, so comparisons are straightforward here.

  2. 02

    Dimensions drive storage

    Bigger vectors cost more to store and search. Smaller dimensions with good recall often win in production.

  3. 03

    Keep it stable

    Changing embedding models means re-indexing everything. Choose one you can live with.

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