Fine-Tuning Cost Calculator
Calculate the costs of fine-tuning and running your custom LLM models.
When to Fine-Tune
- You have domain-specific terminology or style requirements
- You need consistent formatting or structure in outputs
- Your use case requires specialized knowledge not in base models
- You want to reduce prompt length and save on inference costs
Training Configuration
Expected Inference Usage
Cost Breakdown
Fine-Tuning Best Practices
- Start with at least 50-100 high-quality training examples
- Use 3-4 epochs - more can lead to overfitting
- Validate your model with a test set before deploying
- Fine-tuning works best for style, format, and tone adjustments
- Consider if prompt engineering can achieve similar results first
- Monitor fine-tuned model performance over time
Is fine-tuning worth the cost?
Fine-tuning adapts a base model to your data so it follows a specific style, format, or domain more reliably — and often with shorter prompts, since the instructions are baked into the weights. It has two cost components: a one-time training charge based on your dataset size and the number of epochs, and an ongoing inference rate that is usually higher than the base model's.
This calculator combines both into a first-year estimate, so you can weigh the upfront training spend against the per-request savings from shorter prompts. Fine-tuning pays off when you have a narrow, repetitive task with consistent formatting needs and enough volume to amortize the training cost; for one-off or broad tasks, good prompting or retrieval is usually cheaper and more flexible.
How to use this tool
- Choose a base model and enter your training dataset size and number of epochs.
- Add your expected monthly request volume and average input/output tokens.
- Compare the total first-year cost against running the base model with a longer prompt.
Frequently asked questions
When should I fine-tune instead of just prompting?+
When you need consistent style or structure, have a narrow repetitive task, and want to shorten prompts at high volume. For knowledge that changes often, retrieval-augmented generation is usually a better fit than fine-tuning.
How many epochs do I need?+
Three to four is a common starting point. More epochs can improve fit but risk overfitting and increase training cost linearly, so it's worth experimenting on a small scale first.