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OpenAI Fine Tuning Calculator

Estimate the total budget for an OpenAI fine-tuning project, including training, repeated experiments, and expected monthly inference. The calculator keeps one-time training expense separate from ongoing serving expense so teams can see the full first-month commitment.

Use editable rates for the model and contract you plan to use. Dataset size, epochs, and experiment count should reflect the actual training plan rather than an optimistic minimum.

Workload assumptions

tokens
epochs
runs
USD
tokens
tokens
USD
USD
Result
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First-month total
Training cost—
Monthly serving cost—
Training tokens processed—
Year-one total—

1. Enter workload volume
Use the expected monthly activity rather than a single peak day.

2. Add usage assumptions
Enter average token, frame, document, or conversation values that match the fields shown.

3. Set current rates
Replace the default rates with the pricing that applies to your chosen service and account.

4. Review the main result
The large result shows the primary monthly cost or capacity estimate.

5. Check the breakdown
Use the supporting figures to identify the largest cost or capacity driver.

6. Test alternatives
Change one assumption at a time to compare scenarios and identify practical optimization targets.

Training cost = dataset tokens × epochs × experiment count × training rate ÷ 1,000,000. Monthly serving cost adds input-token and output-token inference charges. First-month total equals training cost plus one month of serving.

All monetary results are estimates based on the user-entered rates. Tokenization, caching, batching, minimum charges, and provider-specific billing rules can change the billed amount.

What the result means

The main figure summarizes the estimated fine-tuning project outcome for the assumptions entered above.

Use measured production data where possible and confirm current provider billing rules before making a purchase or pricing decision.

Given: The example uses the default values shown in the calculator.

Calculation: A 2 million-token dataset trained for 3 epochs across 2 experiments processes 12 million training tokens. At $8 per million, training costs $96. If monthly serving costs $270, the first-month total is $366.

Result: The displayed result provides a planning baseline that can be recalculated with production measurements.

What should I use for fine-tuning project pricing?

Enter the rates that apply to the exact model, service tier, and contract you plan to use. Published prices can change, and negotiated or batch rates may differ.

Should token counts be averages or maximums?

Use representative averages for budgeting and maximums for stress testing. A useful review compares both scenarios because long-tail requests can materially raise cost.

Does this result include every operating expense?

No. The calculator includes only the fields shown on the page. Add separate allowances for engineering, observability, data storage, networking, taxes, and vendor minimums when they apply.

How can I reduce estimation error?

Measure a sample of real requests, calculate average and high-percentile usage, and update the assumptions after a pilot. Revisit the estimate whenever the prompt, model, or workflow changes.

Can I compare different models with this calculator?

Yes. Keep the workload assumptions constant, then replace the token rates or capacity assumptions for each model. Compare both total cost and whether the model meets the required quality and latency targets.