Fine-tune vs prompt break-even
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About Fine-tune vs prompt break-even
Fine-tuning changes both capability and unit economics: providers usually charge a one-time or training-hour fee plus a different per-token table for the resulting custom endpoint. This worksheet keeps the story honest: you supply a training cash outlay, token mix per request, and list-style dollars per million for base versus fine-tuned input and output. We solve for how many requests you must serve before variable-cost savings equal the upfront fee. Hidden costs — data labeling, eval harnesses, drift monitoring — still belong in your spreadsheet.
Key Features
- One-time fee input
- Independent base vs fine-tuned $/M for input and output
- Break-even request count
Use Cases
- Deciding whether to fine-tune a support bot
- Finance review of GenAI CapEx
Pro Tips
- If output price dominates, small improvements on input $/M barely move break-even.
Frequently Asked Questions
Does this include hosting the fine-tuned weights?
No — only the fee you type and per-request token pricing.