Fine-Tuning Cost Calculator
Estimate what a fine-tuning run will cost, what the fine-tuned model adds to your inference bill, and whether a longer prompt would have been cheaper.
1. Training run
0
Total training tokens
$0
Cost per epoch
$0
Total training cost
2. Inference uplift vs base model
| Monthly inference | Base model | Fine-tuned model | Extra per month |
|---|
3. Break-even vs prompt engineering
How to read this: training cost = dataset tokens x epochs x training price per 1M tokens. The preset training prices are example rates only - approximate, as of July 2026, check your provider's fine-tuning pricing page. Many providers also charge a higher per-token rate to serve a fine-tuned model (the multiplier in section 2), and some add hourly hosting for dedicated deployments, which this tool does not model. The break-even section compares fine-tuning against the usual alternative: stuffing instructions and few-shot examples into every prompt. If fine-tuning lets you delete those tokens from each request, the monthly saving is saved-tokens x requests x base input price; break-even is training cost plus any monthly uplift measured against that saving. Rule of thumb: try prompt engineering and prompt caching first - fine-tune when you need consistent style/format at scale, shorter prompts, or behavior the base model will not follow reliably.