How is fine-tuning training cost calculated?

Training cost equals total training tokens multiplied by the model's per-token training rate. Total training tokens equal examples times average tokens per example times epochs, since every example is seen once per epoch.

When does fine-tuning pay off versus prompting?

Fine-tuning replaces a long instruction or few-shot prompt with a shorter one, saving input tokens on every call. It pays off once cumulative inference savings exceed the one-off training cost, which the calculator estimates as a breakeven request count.

Does fine-tuning add inference cost?

Yes. Fine-tuned models usually carry a higher per-token inference price than their base versions. The calculator focuses on training cost and prompt-length savings; confirm your fine-tuned inference rate before final budgeting.

No. The estimator runs entirely in your browser. You enter only counts and settings, never your training data, and nothing is stored or sent anywhere.

Fine-Tuning Cost Estimator

Fine-tuning cost estimator

Fine-tuning trades a one-off training bill for cheaper, shorter prompts on every future call. This estimator computes the training cost from your dataset size and epochs, then shows the breakeven — how many requests you must make before the prompt-token savings from a fine-tuned model repay the training investment.

How it works

Training cost is driven by total training tokens: examples times average tokens per example times epochs (each example is processed once per epoch). Multiplying by the model’s training rate gives the one-off cost.

The breakeven side assumes fine-tuning lets you drop a long instruction or few-shot prompt. The calculator estimates the input tokens you save per request and divides the training cost by the per-request saving to find how many calls recover the investment. If you make more calls than that, fine-tuning wins.

Tips and notes

Small, clean datasets beat large noisy ones. A few hundred high-quality examples often outperform thousands of weak ones — and cost far less to train.
Account for higher inference rates. Fine-tuned models usually cost more per token at inference than their base versions; if your prompt savings are small, that premium can erase the benefit.
Rates are editable estimates. Provider training and inference prices change often — confirm the current numbers before committing budget.