Work out embedding generation cost instantly with clear inputs, formula shown and shareable results.
Embedding is billed per input token, so the total is documents times tokens per document times the price. It is a one-off cost for a static corpus but recurs on every re-index, which is what makes changing embedding model an expensive decision. The resulting vector payload is documents times dimensions times bytes per value and is often the larger long-term cost.
Embedding cost
tokens = documents x tokens per document; cost = tokens x price / 1,000,000; vector bytes = documents x dimensions x 4
Yes, entirely. Vectors from different models live in incompatible spaces, so a model upgrade means a full re-index of the corpus.
Models trained with Matryoshka representation learning support truncating the vector to a shorter prefix with modest recall loss, which cuts storage without re-embedding.