Fine-tune vs Prompting ROI
When does fine-tuning pay off?
📚 Learn more — how it works, FAQ & guide Click to expand
Learn more — how it works, FAQ & guide
Click to expand
Fine-tune vs prompting ROI calculator
Calculate when fine-tuning pays back vs staying on prompt engineering.
How to use this tool
- 1
Enter current prompt size
Few-shot examples + instructions you pay for every request.
- 2
Enter expected prompt after fine-tune
Typically much smaller — the model learned the task.
- 3
See break-even queries
When fine-tune training cost pays itself back.
Frequently Asked Questions
When is fine-tuning worth it?
High query volume + consistent task + acceptable quality with shorter prompts. Rule of thumb: >100K queries/month of the same task type, and you can cut prompt by 50%+, fine-tuning pays back in 2-4 months.
What about inference premium?
Most providers charge slightly more for fine-tuned model inference. OpenAI: about 2-6× base model price. Anthropic: similar. This calculator factors that in.
Alternatives to fine-tuning?
Before fine-tuning: try prompt caching (90% off), few-shot compression, better few-shots, RAG to reduce context, routing easier queries to smaller models. Fine-tuning is powerful but capital-intensive.
You might also like
🔒
100% Privacy. This tool runs entirely in your browser. Your data is never uploaded to any server.