RAG Embedding Cost Estimator
Estimate embedding cost from document count, chunking settings, and embedding token price.
Last reviewed by the Radiatus Cloud team
Output
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Estimate the cost of embedding a corpus
Building a retrieval system means embedding every document, which has a token cost that scales with the corpus. This tool estimates embedding cost from the document count, chunking settings and the embedding token price, so you can budget before you index.
Why embedding cost is easy to underestimate
Embedding a corpus for retrieval means turning every chunk of every document into a vector, and the token count is the total text across all chunks, including the overlap between them, which adds up faster than the raw document count suggests. Multiplying that by the per-token embedding price gives the one-off indexing cost, and re-embedding after a change repeats it. Estimating this before you start avoids a surprise bill on a large corpus, and it lets you see how chunk size and overlap, which affect the total token count, change the cost.
Budget before indexing
The tool runs entirely in your browser, so nothing you paste, prompts, outputs or documents, is uploaded, which matters when the input is sensitive AI data or your own content.
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Frequently Asked Questions
What drives the embedding cost?
The total tokens across all chunks of all documents, including the overlap between chunks, multiplied by the per-token embedding price.
Why is it easy to underestimate?
Because overlap between chunks and the sheer text across a corpus add up faster than the document count suggests, especially at scale.
Does chunking affect the cost?
Yes. Smaller chunks with more overlap mean more total tokens to embed, so chunk settings change the cost as well as retrieval quality.
Is it a one-off cost?
Largely yes for a static corpus, but re-embedding after changes repeats it, so budget for updates as well as the initial index.
Is my data uploaded?
No. The estimate runs entirely in your browser.
Privacy & Security
Calculated locally in your browser. No data is sent to any server.
How to Use
Enter corpus and chunking assumptions to estimate embedded tokens and cost.
Disclaimer: This tool is provided "as is" without warranty of any kind. Results are for educational and utility purposes.
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