Is fine-tuning worth the work?
Fine-tuning is a project, not a free shortcut to better answers. The decision gets easier when you can name a recurring failure and measure whether training fixes it.
Make the baseline strong
Try a clear prompt and an appropriate retrieval setup first. Build a held-out evaluation set that captures the failure you want to solve. A model that simply remembers training examples is not evidence of a useful improvement.
Price more than training
Include dataset preparation, review, experimentation, evaluations, and ongoing inference. Confirm which models support the tuning method you need. Availability differs across providers; do not assume every hosted model can be fine-tuned.
Set a payback test
Estimate how many requests are needed to recover the investment through lower cost or better outcomes. Plan how the tuned model will be updated. Verify that any credits cover both the training and serving services before treating them as a project budget.