Fine-Tuning Cost Estimate Calculator
Estimate what a fine-tuning job costs from your dataset size, epochs and the provider’s per-million-token training rate — the tokens-processed model most APIs bill on.
How it works
Tokens trained = dataset tokens × epochs · Cost = tokens trained (in millions) × price per 1M tokens
Most managed fine-tuning APIs bill on tokens processed, which is your dataset size multiplied by the number of epochs — so three passes over a 10M-token file is billed as 30M tokens. The training rate is typically higher than the model’s normal input pricing, reflecting the extra compute of gradient updates. Fewer epochs and a tighter, deduplicated dataset are the two cleanest ways to cut the bill; the later epochs often add the least accuracy per dollar, so start low and add passes only if validation loss is still falling.
Worked example
A 10M-token dataset trained for 3 epochs processes 30M tokens. At $8 per million tokens, that is 30 × $8 = $240, or $80 per epoch.
Frequently asked questions
How many epochs should I use?
Three to four is a common starting point. Watch validation loss: if it keeps dropping, another epoch may help; if it flattens or rises, you are overfitting and paying for nothing. Fewer, higher-quality examples usually beat more epochs.
Why is fine-tuning priced higher than inference?
Training runs a forward and a backward pass and updates weights, which is far more compute per token than inference’s single forward pass. That is why the per-million-token training rate is usually several times the model’s input rate.
Does this include the cost of using the fine-tuned model?
No. This is the one-time training cost. Running the tuned model afterward is billed at its own (often slightly higher) inference rate — estimate that with a tokens-per-dollar or token-cost calculator.
Related calculators
Educational estimate — real costs and memory depend on your provider, framework, model architecture and pricing, which change often. Verify against your own bills and profiling before committing budget.