SourceVane · Practical AI
Measure AI API retry cost before changing models
Count failed attempts, final successful tasks and token use separately so retries do not hide the real cost of useful API work.
Direct answer
The decision in brief
Include every charged attempt in the numerator and only accepted final results in the denominator. This exposes whether retries, timeouts or rejected outputs make a low token price more expensive for the workload that actually succeeds.
Which retry numbers belong in an API cost estimate?
- Export the billed input and output tokens for every attempt, including failures and timeouts that were charged.
- Calculate average attempts per original task. Record final accepted tasks with a written acceptance rule; do not treat HTTP success as useful output.
- Compare monthly token cost and cost per successful task. Change one retry policy at a time, then repeat the same representative workload.
Use these as evaluation steps for your own workload. Record the evidence and limits before acting on the result.
Review the reproducible scenario and dataset · Calculate with your own workload
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Reduce AI API costs without losing useful results
Measure workload, retries and accepted outputs before comparing models, batching or reusable prompts.
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Illustrative retry example
Conclusion: Under these assumptions, 1.2 billed attempts raise token spend from $9.00 to $10.80 per 1,000 original tasks. That is a 20% increase with token use and accepted tasks held fixed.
These are illustrative assumptions, not provider quotes or measured success rates: 2,000 input and 500 output tokens per attempt, $2 per million input tokens, $10 per million output tokens, and 800 accepted final tasks. Cost per accepted result is $0.01125 at one attempt and $0.01350 at 1.2 attempts.
Real retries may use different token counts or improve acceptance. Sum actual billed tokens and measure accepted original tasks after the retry policy; never count attempts as successful tasks.
Calculate your own scenario · Inspect the scenario study and downloadable inputs.
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Evidence and next steps
Reduce AI API costs without losing useful results
Measure workload, retries and accepted outputs before comparing models, batching or reusable prompts.
How retries change AI API cost per successful task
A reproducible scenario analysis separates token spend, average attempts and final successful tasks across four retry assumptions.
AI API pricing comparison
Compare selected first-party text API list prices using the same per-million-token unit, with conditions, verification dates and official sources.
AI API cost calculator
Compare two AI API budgets using token prices, retries and successful tasks. Enter your own assumptions; no API calls or account required.
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