With Amazon SageMaker, you pay only for what you use. Building, training, and deploying ML models is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in hosting instances.
Try Amazon SageMaker in AWS China (Ningxia) Region for two months, free!
As part of the AWS Free Tier, you can get started with Amazon SageMaker in AWS China (Ningxia) Region for free. If you have never used Amazon SageMaker before, for the first two months, you are offered a monthly free tier in AWS China (Ningxia) Region of 250 hours of t2.medium or t3.medium notebook usage for building your models, plus 50 hours of m4.xlarge or m5.xlarge for training, plus 125 hours of m4.xlarge or m5.xlarge for deploying your machine learning models for real-time inferencing and batch transform with Amazon SageMaker. Your AWS China (Ningxia) Region free tier starts from the first month when you create your first SageMaker resource.
Included with Amazon SageMaker Training and Hosting
When you train your models in Amazon SageMaker and enable Amazon SageMaker Debugger, you can use built in rules for debugging or write your own custom rules, or both. SageMaker Debugger provides a fully managed experience for running both built-in and custom rules as Amazon SageMaker Processing jobs. For built-in rules, there is no charge and Amazon SageMaker Debugger will automatically select an instance type. For custom rules, you will need to choose an instance (e.g. ml.m5.xlarge) and you will be charged for the duration for which the instance is in use for the Amazon SageMaker Processing job.
When you deploy your models as Amazon SageMaker endpoints for real-time inference and enable Amazon SageMaker Model Monitor, you can use built-in rules to monitor your models or write your own custom rules, or both. Model Monitor provides a fully managed experience for running both built-in and custom rules as Amazon SageMaker Processing jobs. For built-in rules with ml.m5.xlarge instance in China (Ningxia) Region, you get up to 30 hours of monitoring aggregated across all endpoints each month, at no charge. Usage in China (Beijing) Region, usage beyond 30 hours in China (Ningxia) Region, or usage for other ML instance types will be charged for the duration for which the instance is in use at the Amazon SageMaker Processing on demand rate.
Choice of Amazon EC2 On-demand and Spot Instances
With Amazon SageMaker you have the choice of choosing from Amazon EC2 On-Demand instances or Amazon EC2 Spot instances. For building, training, and deploying your models on Amazon SageMaker, on-demand ML instances let you pay for machine learning compute capacity by the second, with no long-term commitments. This frees you from the costs and complexities of planning, purchasing, and maintaining hardware, and transforms what are commonly large fixed costs into much smaller variable costs. Pricing is per instance-hour consumed for each instance, from the time an instance is available for use until it is terminated or stopped. Each partial instance-hour consumed will be billed per-second.
For training your ML models, you have the choice of using Amazon EC2 Spot instances with Managed Spot Training. This option can help reduce the cost of training your machine learning models by up to 90%. Once a Managed Spot Training job completes, you can calculate the cost savings as the percentage difference between the duration for which the training job ran and the duration for which you were billed. The cost savings is also visible on the AWS management console.
ML General Purpose storage
For model training, Amazon SageMaker provides you with the ability to select up to 6 TB of associated General Purpose (SSD) storage capacity for your training data. For notebook, model training, and model hosting, General Purpose (SSD) storage capacity is also added for temporary data storage. With General Purpose (SSD), you will be charged for this storage. However, you will not be charged for the I/Os consumed.
When hosting your models, data processed by Amazon SageMaker is pulled into and out of model hosting instances.