Fine-Tuning Cost Calculator
Calculate your fine-tuning costs by providing your own training rate from your AI provider
Model Selection
Check your provider's pricing page for accurate training rates. OpenAI Pricing →
Training Data
Min 10, recommended 50-100+
prompt + completion tokens
Default: 3-4 epochs
Monthly Inference Estimate
Summary
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Complete Guide to Fine-Tuning AI Models
How to Use This Tool
The Fine-Tuning Cost Estimator helps you plan and budget for custom model training. Follow these steps to calculate your costs:
Select Provider and Model
Choose the AI provider and base model for fine-tuning. Currently OpenAI models are supported, with GPT-4o mini being the most cost-effective and GPT-4o offering the highest quality.
Configure Training Data
Enter the number of training examples you have, the average tokens per example (including both prompt and completion), and the number of training epochs (typically 3-4).
Estimate Monthly Inference
Optionally estimate your monthly inference costs by entering expected request volume and average token counts. Fine-tuned models have different inference pricing than base models.
Review Cost Breakdown
View your one-time training cost, monthly inference costs, and first month total. Copy the results or adjust parameters to explore different scenarios.
What is Fine-Tuning?
Fine-tuning is the process of training a pre-existing AI model on your specific data to customize its behavior. Unlike prompt engineering, which guides the model through instructions, fine-tuning actually modifies the model's weights to permanently learn from your examples.
Fine-Tuning Pricing
| Model | Training/M | Input/M | Output/M |
|---|---|---|---|
| GPT-4o mini | $3.00 | $0.30 | $1.20 |
| GPT-4o | $25.00 | $3.75 | $15.00 |
| GPT-3.5 Turbo | $8.00 | $3.00 | $6.00 |
When to Fine-Tune
Good Use Cases
- Consistent style/tone that prompting can't achieve
- High volume applications (1000s of requests/day)
- Reducing prompt size to save tokens
- Specialized domain knowledge
- Latency-sensitive applications
When to Avoid
- Few-shot prompting works well enough
- Need to frequently update the knowledge
- Low request volume (less cost-effective)
- Need factual knowledge updates
- Limited quality training data
Training Data Requirements
Data Guidelines
- Minimum examples: 10 required, 50-100 recommended for basic tasks
- Format: JSONL with
{"messages": [...]}structure for chat models - Diversity: Cover various inputs and edge cases
- Quality: Examples should be high-quality ideal outputs
- Consistency: Maintain consistent style across all examples
Understanding Epochs
An epoch represents one complete pass through your training data. The number of epochs affects both cost and quality:
- • 1-2 epochs: Light training, subtle behavior changes.
- • 3-4 epochs: Recommended default, good balance of learning and cost.
- • 5+ epochs: Deeper training, risk of overfitting to training data.
Cost Optimization Tips
- • Start small: Begin with fewer examples and add more if needed.
- • Use GPT-4o mini: 8x cheaper training than GPT-4o with excellent results.
- • Keep examples concise: Shorter examples = lower training costs.
- • Validate before training: Test your data format to avoid failed jobs.
- • Iterate gradually: Train with 3 epochs first, then adjust if needed.
Fine-Tuning vs Alternatives
| Approach | Upfront Cost | Per-Request Cost | Best For |
|---|---|---|---|
| Fine-Tuning | $$ (training) | Lower tokens | High volume, consistent style |
| Few-Shot Prompting | Free | Higher tokens | Flexibility, quick iteration |
| RAG | $ (vectors) | Higher tokens | Dynamic knowledge |