Fine-Tuning Cost Calculator
Estimate the training cost and ongoing inference cost of fine-tuning an OpenAI language model.
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How to use this calculator
- 1
Select the base model you plan to fine-tune.
- 2
Enter the number of training examples and their average token length.
- 3
Enter the number of epochs (training passes). More epochs = more cost and sometimes better results.
- 4
Enter your expected monthly inference volume to see the ongoing cost alongside the one-time training cost.
Frequently asked questions
How many training examples do I need?
OpenAI recommends starting with 50–100 high-quality examples and scaling up from there. For most tasks, 200–1,000 examples strike the right balance between cost and performance. Doubling examples does not always double quality — focus on example diversity and quality.
What is an epoch in fine-tuning?
An epoch is one full pass through your training dataset. The model sees each example once per epoch. More epochs can improve performance on small datasets but risk overfitting. 3–5 epochs is a common starting point.
Is fine-tuning worth it vs prompting?
Fine-tuning is worth it when you have a consistent task type and quality bar that prompting alone cannot achieve, or when you want to reduce input token costs by encoding knowledge into the model weights. For most use cases, advanced prompting and retrieval-augmented generation (RAG) should be tried first.
Fine-Tuning Cost Calculator — GPT-4o, GPT-3.5, and More
Training cost vs inference cost
Fine-tuning involves two distinct cost components: a one-time training cost paid when you run the fine-tuning job, and ongoing inference costs every time you call the fine-tuned model. For low-volume applications, the training cost may dominate. For high-volume production apps, monthly inference costs quickly dwarf the training spend.
When fine-tuning reduces inference costs
A well-fine-tuned model can often achieve the same output quality with a shorter system prompt, which reduces input token costs per request. If your current system prompt is 2,000 tokens and fine-tuning shrinks it to 200 tokens, you save 1,800 input tokens on every single request — potentially making fine-tuning cost-positive within weeks.
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →