Fine-Tuning vs Prompting Cost Calculator
Is fine-tuning cheaper than few-shot prompting at your volume? Calculate training cost, monthly inference savings, and break-even in months.
Prices verified 2026-08-20 · Reduce few-shot tokens first
How to Use
Set training data
Enter number of training examples, average tokens per example, and number of training epochs (typically 3–5).
Set your comparison
Choose the fine-tuned model and the baseline few-shot prompted model. Enter how many extra tokens the few-shot examples add per call.
Enter request volume
Monthly requests, average input tokens, and average output tokens. Higher volume makes fine-tuning more attractive.
Find the break-even
The break-even in months tells you how long before fine-tuning ROI justifies the upfront training cost.
Fine-tuning economics
Fine-tuning has a one-time training cost but reduces per-call cost by eliminating few-shot examples (the model "knows" the format after training). The break-even depends entirely on how many extra tokens your few-shot examples add and how many requests you make per month. At low volume (<10k/month) or short prompts, few-shot prompting is almost always cheaper. At high volume (>100k/month) with long few-shot examples, fine-tuning typically pays back in 3–6 months.
How to Cut This Cost
Use an open-weight model (Llama, Mistral) for fine-tuning. Training costs are just GPU time — no per-token training fees. Inference is then free beyond hardware.
Reduce few-shot examples from 10 → 3 while maintaining quality. Each example removed is tokens saved on every inference call permanently.
Try prompt engineering + caching before fine-tuning. Caching long few-shot blocks costs 10% of full price — often cheaper than the training investment.
FAQ
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Last verified: 2026-08-20 · methodology · data sources