# Infrastructure options
<a name="customizing-models-infrastructure"></a>

Amazon SageMaker AI offers three infrastructure options for model customization. Choose based on your requirements for control, scale, and capacity access. For example, if you have reserved compute capacity through [SageMaker AI Flexible Training Plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html), you can use the customization Recipes to run customization jobs on your reserved capacity. The difference is in how compute resources are managed.

**Recipes** are pre-configured training configurations that work with both SageMaker AI Training Jobs and HyperPod. They automate dataset loading, distributed training, and checkpoint management. See [SageMaker Recipes](https://github.com/aws/sagemaker-hyperpod-recipes) on the GitHub website on GitHub.


| Option | What it is | Interfaces | Best for | 
| --- | --- | --- | --- | 
| [Serverless model customization](customize-model.md) | Fully managed, no instance selection. Infrastructure provisioned and released automatically. | Studio UI, Python SDK (SFTTrainer, DPOTrainer, etc.) | Quick experimentation, production jobs without ops overhead | 
| [SageMaker AI Training Jobs](customizing-models-training-jobs.md) | Ephemeral jobs where you select instance type and count. Instances released after training. | Recipes, Python SDK (ModelTrainer) | Custom instance configs, advanced distributed setups | 
| [HyperPod](customizing-models-hyperpod.md) | Persistent clusters with automatic fault detection and recovery. Clusters remain available across jobs. | Recipes, HP-CLI | Large-scale training, long-running jobs, multi-job workflows |