AI & LLM cost
Fine-tuning vs. inference cost
Fine-tuning cost is a one-time or periodic training expense to specialize a model. Inference cost is the ongoing, usage-based cost of running it.
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Definition
Fine-tuning takes a base model and trains it further on your own examples. Training data volume and compute time drive its cost. You pay it once per training run, not per use. Inference is what happens each time the resulting model answers a request. It is priced like any other model call, per token and per request.
Do not model training cost in isolation. Ask whether the fine-tuned model's inference cost pays back the upfront training investment across the request volume you expect. Inference is often cheaper, because a smaller specialized model can sometimes replace a larger general one.
Where it shows up
Fine-tuning cost appears on the provider's invoice as a one-time training job charge. The provider prices it by training data volume and compute time for that run. Inference cost for the resulting model appears afterward as ordinary per-token usage. It applies to every call the model serves.
What makes it expensive
Training cost is the easy number to model. On its own, it answers nothing. What matters is whether the fine-tuned model's inference cost pays the training back across the request volume you really expect. Put its per-request cost next to the general model it replaced. Then divide the training bill by the difference.
Related
Token spend moves every day.
Model prices and rate limits change between invoices. CostMon tracks your AI spend daily, so a change shows up before the bill does.