As businesses and IT leaders look to accelerate the adoption of machine learning (ML), there is a growing need to understand spend and cost allocation for your ML environment to meet enterprise requirements. Without proper cost management and governance, your ML spend may lead to surprises in your monthly AWS bill. Amazon SageMaker is a fully managed ML platform in the cloud that equips our enterprise customers with tools and resources to establish cost allocation measures and improve visibility into detailed cost and usage by your teams, business units, products, and more.
In this post, we share tips and best practices regarding cost allocation for your SageMaker environment and workloads. Across almost all AWS services, SageMaker included, applying tags to resources is a standard way to track costs. These tags can help you track, report, and monitor your ML spend through out-the-box solutions like AWS Cost Explorer and AWS Budgets,

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