Prompt compression ROI calculator
A few hundred wasted tokens in a prompt feel trivial — until you multiply them by tens of thousands of daily calls. Prompt compression (trimming boilerplate, summarizing context, or using a library like LLMLingua) attacks exactly that recurring cost. This calculator turns a before-and-after token count into concrete daily, monthly, and yearly savings so you can decide whether the engineering effort pays off.
How it works
Compression only changes input tokens, which are billed per million. The saving per
call is (original - compressed) tokens, priced at your model’s input rate. Because every
call pays the prompt cost, that per-call saving multiplies directly by your daily volume:
saved per call x calls per day gives the daily saving, scaled to 30 days and 365 days
for the monthly and yearly figures. The tool also reports the compression ratio so you
can see how aggressive your reduction is.
Tips and notes
- A 40% prompt reduction at high volume often saves more than switching to a cheaper model — and keeps your accuracy on the model you already validated.
- Always re-run your eval set after compressing; the savings here are only real if the shorter prompt produces equivalent answers.
- Combine compression with prompt caching: cache the stable prefix and compress the variable part for compounding savings.