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.
Worked example
Suppose your current prompt is 1,500 tokens. After manual trimming, it drops to 900 tokens — a 600-token saving per call. You make 20,000 calls per day. At an input price of $1.50 per million tokens:
- Saving per call: 600 tokens
- Daily saving: 600 × 20,000 ÷ 1,000,000 × $1.50 = $18/day (for example)
- Monthly saving: approximately $540
- Yearly saving: approximately $6,570
This is the arithmetic the calculator runs for your specific numbers. Notice that the engineering time to trim a prompt from 1,500 to 900 tokens is usually measured in hours, not days — the ROI horizon in this kind of example is often less than a week.
When compression beats switching models
A common alternative to compression is switching to a cheaper model. The tradeoff:
- Compression keeps quality constant on the model you already tested.
- A cheaper model changes quality in ways that need a new evaluation cycle.
If you have already validated quality on your current model, compression is often the faster and lower-risk path to savings — there is no new eval round required as long as the shorter prompt still meets your accuracy bar.
When to think about LLMLingua or semantic compression
Manual prompt trimming — removing boilerplate, deduplicating context, cutting filler — is fast and transparent. It works well up to roughly 30–40% reduction. Beyond that, you start cutting load-bearing context, and quality degrades. At that point, a learned compression model like LLMLingua can achieve deeper ratios by identifying which tokens matter most for the task. This calculator applies equally to both approaches; you just input different before/after token counts.
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.
- Track the compression ratio alongside the savings. A 60% ratio is aggressive; verify quality before treating the dollar figure as bankable.