Fine-Tuning Cost Calculator
Estimate LLM fine-tuning costs from dataset size, epochs, and model choice for OpenAI API and self-hosted options.
Inputs
%
Results
Total estimated cost ($)
$1,671.17
Training/compute cost ($)$4.5
Dataset prep cost ($)$1,666.67
Training tokens (millions)1.35
Est. training time (hours)1.4
Dataset prep hours33.3
Validation examples100
Prompt token savings (%)40%
Inference Price Per1M$0.3
How to Use This Calculator
- Enter the number of Training Examples (prompt-completion pairs) in your dataset — typically 500–5,000 for most fine-tuning tasks.
- Set average Tokens per Example (prompt + completion combined) and the number of Training Epochs (2–4 is typical).
- Select the Model — API-based fine-tuning (GPT-4o mini, GPT-3.5) or self-hosted (Llama, Mistral).
- Set the Validation Split percentage to hold out data for evaluation.
- Review Total Estimated Cost, Training Tokens, Estimated Training Time, and Dataset Prep Hours to plan your fine-tuning project.
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