Free AI operating-cost tool
Estimate a 30-day LLM API token bill.
Compare a large-model baseline with a hypothetical routing and caching strategy. Enter your own workload and provider prices. All defaults are illustrative, not live prices or verified savings.
Your assumptions
No account or data upload required.
30-day example · USD
Scenario comparison
Large model only; no caching
$2,400.00
Blended routing and eligible caching
$1,021.29
Modelled cost difference
$1,378.71
57.4% lower than baseline
300,000 calls over 30 days. Excludes embeddings, RAG retrieval, tools, retries, servers, taxes and staff. Lower cost does not demonstrate acceptable quality or latency.
How the estimate works
Baseline: 30 × daily calls × (input tokens × large input rate + output tokens × large output rate) ÷ 1 million.
Blended: use the weighted large/small input and output prices. Only the input portion receives the modelled cache discount: 1 − (eligible input fraction × input discount). Multiply by tokens and monthly calls.
This assumes the same average token volumes across routes, uniform caching eligibility and no extra fallback traffic. It is a sensitivity model, not a billing forecast. Validate actual model usage and response quality.
What to measure in production
- Actual input and output tokens by feature and model
- Cache-eligible token counts and invoiced discounts
- Answer quality, fallback rates, latency and errors
- Retrieval, storage, infrastructure and monitoring costs
Read Anant's enterprise LLM cost architecture essay and RAG-versus-fine-tuning analysis for implementation context.
Need a production AI cost review?
Discuss workload measurement, routing safeguards and unit economics with Anant Mishra, IAG technology executive and Invadecore CEO.