Fine-tuning has upfront costs but can reduce long-term expenses.
Upfront costs:
- Compute: - depending on model size and method
- Data preparation: Hours to weeks of effort
- Iteration: Multiple training runs to get it right
Ongoing savings:
- Shorter prompts (fewer input tokens)
- No retrieval latency
- Better task performance
Break-even typically happens after - months of production use at scale.