Embedding Cost Calculator
Estimate the cost of embedding your document corpus for search or retrieval.
Corpus
$0.02/M tokens
Indexing cost
$0.30per full index
Total tokens embedded
15,000,000
Estimated monthly cost
$0.30
This covers indexing only (turning your corpus into vectors). Query-time embedding and LLM generation cost are estimated separately in the RAG Cost Calculator.
How this is calculated
cost = documents × average tokens per document ÷ 1,000,000 × price per million tokens. Multiply by re-indexes per month for an ongoing monthly figure.
Pricing is verified against provider pricing pages as of 2026-09-01. AI and cloud pricing changes frequently — confirm the current rate on the provider's own pricing page before budgeting.
Frequently asked questions
Why is embedding so much cheaper than chat completion?
Embedding models only produce a fixed-size vector, not generated text, so there's no expensive output-token cost — pricing is a single low per-token input rate, often 50-250x cheaper than LLM output pricing.
How should I choose a chunk size?
Smaller chunks (200-400 tokens) retrieve more precisely but need more chunks per query; larger chunks (800-1500 tokens) capture more context per chunk but can dilute retrieval relevance. Chunk size also directly sets your indexing token count.
Do I need to re-embed my whole corpus regularly?
Only if the underlying documents change. A static knowledge base needs one indexing pass; a frequently updated corpus (docs, tickets, changing product data) needs periodic re-indexing, which this calculator lets you factor in as a monthly cost.
Which embedding model should I use?
text-embedding-3-small is the cheapest and fine for most retrieval use cases; text-embedding-3-large trades roughly 6x the cost for higher-dimensional, generally more accurate embeddings on harder retrieval tasks.