RAG cost simulator
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About RAG cost simulator
Retrieval-augmented generation has three big cost centers: embedding and storing chunks, paying for retrieved tokens on every query, and generating answers. This simulator multiplies your assumed corpus shape (documents × chunks × tokens per chunk) by a single reference model's list prices as a crude stand-in for both embedding and generation economics — accurate systems usually use a small embedding model with its own price card. Treat the output as a scenario for discussions, not an invoice.
Key Features
- Corpus chunk math
- Top-K retrieval token estimate
- Daily query volume to monthly
- Generation output budget
Use Cases
- Architecture review slide decks
- Comparing dense vs smaller chunk strategies
Pro Tips
- If you re-embed nightly, multiply ingest by refresh frequency.
Frequently Asked Questions
Why one model for everything?
Simplicity; split embedding vs chat models in your own sheet for accuracy.