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Overview

This includes both training and validation processes, and varies based on the model size, fine-tuning type (Supervised Fine-tuning or DPO), and implementation method (LoRA or Full Fine-tuning).

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How Pricing Works

The total cost of a fine-tuning job is calculated using:
  • Model size (e.g., Up to 16B, 16.1-69B, etc.)
  • Fine-tuning type (Supervised Fine-tuning or Direct Preference Optimization (DPO))
  • Implementation method (LoRA or Full Fine-tuning)
  • Total tokens processed = (n_epochs × n_tokens_per_training_dataset) + (n_evals × n_tokens_per_validation_dataset)
Each combination of fine-tuning type and implementation method has its own pricing. For current rates, refer to our fine-tuning pricing page.

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Token Calculation

The tokenization step is part of the fine-tuning process on our API. The exact token count and final price of your job will be available after tokenization completes. You can find this information in:
  • Your jobs dashboard
  • Or by running together fine-tuning retrieve $JOB_ID in the CLI

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Frequently Asked Questions

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Is there a minimum price for fine-tuning?

No, there is no minimum price for fine-tuning jobs. You only pay for the tokens processed.

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What happens if I cancel my job?

The final price is determined based on the tokens used up to the point of cancellation.

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Example:

If you’re fine-tuning Llama-3-8B with a batch size of 8 and cancel after 1000 training steps:
  • Training tokens: 8192 [context length] × 8 [batch size] × 1000 [steps] = 65,536,000 tokens
  • If your validation set has 1M tokens and ran 10 evaluation steps: + 10M tokens
  • Total tokens: 75,536,000
  • Cost: Based on the model size, fine-tuning type (SFT or DPO), and implementation method (LoRA or Full FT) chosen (check the pricing page)

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How can I estimate my fine-tuning job cost?

  1. Calculate your approximate training tokens: context_length × batch_size × steps × epochs
  2. Add validation tokens: validation_dataset_size × evaluation_frequency
  3. Multiply by the per-token rate for your chosen model size, fine-tuning type, and implementation method