Embedding cost

Embeddings are the cheapest per-token models on the market — which is exactly why teams forget to budget them. At search scale, query embeddings run every single day, and the refresh loop never stops.

Corpus: pages / documents

Average words per page

Corpus refresh per month %

Embedding model

Search queries per day

Tokens per query

Vector database $/month

The formula

corpus_tok = pages × words × 1.33 initial = corpus_tok/1M × E_in (one-time) refresh = initial × refresh% (monthly) queries = queries/day × qTok/1M × E_in × 30 (monthly) total = queries + refresh + vectorDB

Embedding prices as of , pulled daily from OpenRouter's API. Chunking with overlap inflates indexed tokens ~10–15%; embeddings of the same corpus with a new model version are a full re-index — the refresh% input is where you budget for that migration risk.