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Fine-Tuning Cost Calculator
Estimate what a fine-tune costs to train and run, and when it pays for itself.
Training
Using it
The alternative: a base model with a longer prompt
The few-shot examples and instructions a fine-tune would let you delete from every request.
Quick Tool Overview
The Fine-Tuning Cost Calculator prices a training run from examples, tokens and epochs — through a provider or on rented GPUs — then compares running the tuned model against a base model with a longer prompt, and gives the monthly saving and payback period.
- What it does
- Estimates fine-tuning cost and payback
- You give it
- Dataset size, epochs, prices and monthly traffic
- You get back
- Training cost, monthly costs and payback time
- Best for
- Deciding whether fine-tuning is worth it
About the Fine-Tuning Cost Calculator
Fine-tuning has two bills. The training run is charged once, per training token, and every example counts once per epoch. Then the tuned model is usually charged at a higher rate per token than the base model, for as long as you use it. Whether it is worth it depends on what it lets you stop sending.
Enter the number of examples, tokens per example and epochs, and pick a model to load its training and inference prices — or a self-hosted LoRA run, priced by GPU hours from your training throughput. Then describe your monthly traffic and the calculator prices the tuned model against the alternative: the base model carrying the few-shot examples and long instructions a fine-tune would let you delete.
The result is a training cost, a monthly running cost for each option, the monthly saving and the payback period, along with warnings for datasets too small to beat a good prompt and epoch counts high enough to memorise the examples.
Key features of the Fine-Tuning Cost Calculator
Provider or self-hosted
Price per training token, or GPU hours from your training throughput.
Base model comparison
Weighs the tuned model against a base model carrying the prompt tokens a fine-tune removes.
Payback period
How many days or months of savings cover the training bill.
First-year totals
Training plus twelve months of running costs for both options.
Dataset warnings
Flags datasets below provider minimums and epoch counts that risk memorisation.
How to use the Fine-Tuning Cost Calculator
-
1
Describe the dataset
Number of examples, average tokens per example and number of epochs.
-
2
Choose the model
Its training and inference prices fill in, and every price stays editable.
-
3
Describe your traffic
Monthly requests and tokens per request for the tuned model.
-
4
Compare with the base model
Enter the prompt tokens a fine-tune would save, and read the payback period.
Frequently asked questions
Training tokens are examples multiplied by tokens per example multiplied by epochs, charged at the training price per million tokens. Every message in an example counts, including the system prompt.
OpenAI requires at least 10, but most tasks need 50 to 100 well-chosen examples before a fine-tune reliably beats a good few-shot prompt.
Only when it removes a lot of prompt tokens from every request at high volume. Tuned models cost more per token, so at low volume a longer prompt on the base model is usually cheaper.
Three is a common default. Small datasets may need a few more; past four or five, models tend to memorise examples rather than learn the task.
The GPU rental rate multiplied by training hours. Hours are training tokens divided by throughput in tokens per second, which depends on the card, the model size and the sequence length.
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