Fine-Tuning Cost Estimator
Estimate training and inference pricing for fine-tuning OpenAI and open-source LLMs.
Typical training runs require 3 to 5 epochs to prevent over-fitting.
Expected volume of API input + output tokens processed by your custom model per month.
Educational Purpose Disclaimer
This calculator is provided for informational and educational estimation purposes only. All calculations and projections are based on standard mathematical formulas and assumed inputs. The output values should not be considered professional financial, legal, tax, or medical advice. ProCalc is not a registered financial advisor or licensed practitioner. Always consult a qualified professional (such as a Chartered Accountant, certified planner, or physician) before making major decisions.
What is the Fine-Tuning Cost Estimator?
The Fine-Tuning Cost Estimator helps developers, machine learning engineers, and product teams project the expenses of training custom LLMs. It calculates one-time training fees based on your custom dataset tokens and training epochs, then projects monthly production inference fees to compare against raw base model rates.
How Does the Fine-Tuning Cost Estimator Work?
1. Select Base Model — Choose GPT-4o-mini, GPT-3.5-turbo, Llama 3 8B, or Mistral 7B.
2. Define Dataset Tokens — The number of words/tokens inside your training JSONL files.
3. Set Training Epochs — Number of complete passes the training algorithm makes over the dataset.
4. Input Monthly Volume — Projected generated production tokens per month.
5. Get Total Cost Forecast — Review training fees, monthly inference fees, and first-year net projections.
Formula & Calculation Method
Fine-Tuning Rates (per million tokens):
- Training rates: GPT-4o-mini ($3.00), GPT-3.5 ($8.00), Llama 3 / Mistral ($6.00).
- Inference rates (average blended input/output): GPT-4o-mini ($0.75), GPT-3.5 ($4.50), Llama 3 / Mistral ($1.50).
- Training Fee: (Dataset Tokens × Epochs / 1,000,000) × Training Rate
- Inference Cost: (Monthly Tokens / 1,000,000) × Blended Inference Rate
Example Calculation
Example: Fine-tuning GPT-4o-mini with a 2,000,000-token dataset, running 3 epochs, and generating 10,000,000 tokens per month in production.
- One-time Training Fee: (2,000,000 × 3 / 1M) × $3.00 = $18.00
- Monthly Inference Cost: (10,000,000 / 1M) × $0.75 = $7.50 / mo
- Total Year 1 Cost: $18.00 + ($7.50 × 12) = $108.00.
Frequently Asked Questions
**Fine-Tuning Rates (per million tokens):** - **Training rates:** GPT-4o-mini ($3.00), GPT-3.5 ($8.00), Llama 3 / Mistral ($6.00). - **Inference rates (average blended input/output):** GPT-4o-mini ($0.75), GPT-3.5 ($4.50), Llama 3 / Mistral ($1.50). - **Training Fee:** (Dataset Tokens × Epochs / 1,000,000) × Training Rate - **Inference Cost:** (Monthly Tokens / 1,000,000) × Blended Inference Rate
Disclaimer: This tool is provided for informational and calculation purposes. Output values are estimates based on standard user inputs.