How is the monthly cost calculated?

Monthly cost = requests/day × days/month × ((avg input ÷ 1M × input price) + (avg output ÷ 1M × output price)). Annual cost is the monthly figure × 12.

What is the single biggest lever on cost?

Usually output tokens, since they are billed several times higher than input. Capping max_tokens and asking for concise answers often cuts the bill more than shortening prompts.

Should I budget for spikes?

Yes. This gives a steady-state estimate. Add 20-30% headroom for retries, traffic spikes, and longer-than-average responses, and set hard spend limits in your provider dashboard.

No. The estimator runs entirely in your browser. Nothing you enter is sent to a server or stored.

Monthly AI Spend Estimator

Monthly AI spend estimator

Per-call costs feel tiny — fractions of a cent — until you multiply by thousands of requests a day across a whole month. This tool turns your daily usage pattern into a monthly and annual bill so you can budget, set spend limits, and decide whether a cheaper model or a caching layer pays for itself.

How it works

You provide four numbers: requests per day, days per month the workload runs, average input tokens, and average output tokens. The model preset supplies the input and output prices per million tokens. The estimator computes the cost of one average request, multiplies by your monthly request count, and projects the annual figure. It then highlights where the money goes — input versus output — so you know which lever to pull.

Tips and notes

The most common surprise is output cost dominance: because completion tokens are billed 3-5x higher than input, a chatty model can quietly double your bill. Concrete reductions to try: cap max_tokens, switch routine calls to a mini/flash tier, cache or deduplicate repeated prompts, and batch where the provider offers a discount. Always set a hard monthly spend limit in your provider console — an estimate is not a guardrail.