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Fine-tuning can be intimidating: unlike base models, where you're just paying for input and output tokens for inferencing, fine-tuning requires training your custom models and dealing with hosting. This guide is intended to help you better understand the costs of fine-tuning and how to manage them.
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> [!NOTE]
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> The prices in this article are for example purposes only. In some cases they may match current pricing, but you should refer to the official [pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service) for exact pricing details to use in the formulas provided in this article.
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> [!IMPORTANT]
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> The numbers in this article are for example purposes only. You should always refer to the official [pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service) for pricing details to use in the formulas provided in this article.
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## Upfront investment - training your model
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> [!IMPORTANT]
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> We don't charge you for time spent in queue, failed jobs, jobs canceled prior to training beginning, or data safety checks.
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#### Example: Supervised Fine-Tuning
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#### Example: Supervised fine-tuning (SFT)
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Projecting the costs to fine-tune a model that takes natural language and outputs code.
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