AI Agent Prompt Compression / Token Optimization Cost Calculator
Estimate how much LLM API spend you can save by compressing prompts with summarization, RAG retrieval, caching, truncation, or prompt rewriting.
Scenario presets
Workload and compression settings
Estimates are directional. Last updated: 2026-07-08. See notes.
Uncompressed monthly cost
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Compressed monthly cost
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Monthly savings
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Savings percentage
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Compressor cost / month
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Review cost / month
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Infra cost / month
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Setup cost (one-time)
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Period net savings
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Break-even (months)
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Monthly cost breakdown
| Cost line | Uncompressed | Compressed | Delta |
|---|---|---|---|
| LLM input tokens | — | — | — |
| LLM output tokens | — | — | — |
| Compressor calls | — | — | — |
| Human review | — | — | — |
| Compression infra | — | — | — |
| Total monthly | — | — | — |
Verdict
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Frequently asked questions
What does prompt compression save?
It reduces the number of input tokens sent to the main LLM by summarizing context, retrieving only relevant chunks, truncating old turns, or rewriting verbose prompts. Fewer input tokens means lower API cost and often faster response time.
Does compression always improve quality?
No. Truncation can lose long-range context. Summarization can drop nuance. RAG retrieval usually improves accuracy by focusing on relevant context, but requires a good embedding and chunking setup.
What is the compression ratio?
It is the ratio of compressed input tokens to original input tokens. A ratio of 0.45 means the compressed prompt is 45% as long as the original, cutting input tokens by 55%.
How are compressor costs counted?
Some strategies require a separate cheap-model call to summarize or rewrite the prompt before the main LLM call. The calculator adds that per-request compressor cost and any monthly infrastructure fee.
When is prompt caching better than compression?
Caching is best when a large part of the prompt is identical across requests, such as a system message, few-shot examples, or grounding documents. It gives a discount without losing context, but does not reduce token count.
How is break-even calculated?
Break-even months = one-time setup cost divided by monthly savings net of compression and review costs. If setup cost is zero, the result is immediate.
Prices are approximate per-token list rates as of mid-2026. Compression ratios depend heavily on prompt structure, redundancy, and the compressor model. Treat results as directional for budgeting and ROI prioritization.