AWS Cloud Financial Management

In April 2025, AWS added carbon emissions data to AWS Data Exports. This managed feature introduces the ability to automatically export carbon emissions data with AWS Account and AWS Region granularity on a monthly basis to Amazon Simple Storage Service (S3). When using AWS Organizations, the carbon emissions export delivers data for all member accounts linked to your management account. This blog post explains how to configure the recurring delivery of carbon emissions data to Amazon S3 and visualize the exported data in the sustainability-proxy-metrics dashboard of the Cloud Intelligence Dashboards (CID). Utilizing Data Exports and the CID, you can track emissions across more than one AWS organization, with the ability to build custom visualizations and drill down to member account-level granularity.

AWS announced new capabilities in AWS Budgets that provides greater flexibility in how you track and manage your AWS spend. These enhancements include support for additional cost metrics (net unblended costs and net amortized costs), an ability to exclude specific dimension values when creating budgets (such as services, accounts, and instance types), new filtering capabilities for charge types for fine-grained control to include or exclude AWS Savings Plans (SPs) or Reservation (RI) upfront charges, recurring fees, taxes, and credits, and enhanced API functionality that supports filter expressions that are consistent with AWS Cost Explorer.

Customer Carbon Footprint Tool (CCFT), launched in 2022, is a tool that helps customers track, measure, and review the carbon emissions generated from their AWS usage. The CCFT accounts for Scope 1 and Scope 2 emissions, as defined in the Greenhouse Gas Protocol, covering the full range of AWS products, including Amazon EC2, Amazon S3, AWS Lambda, and more. The emissions are provided as Metric Tons of Carbon Dioxide equivalent (MTCO2e). Today, we are publishing three updates as part of our ongoing process to enhance the CCFT: 1) easier access to carbon emissions data through the Billing and Cost Management Data Exports service, 2) more granular carbon data at the AWS Region level, and 3) updated, independently-verified methodology.

As we continue our five-part series on optimizing costs for generative AI workloads on AWS, our third blog shifts our focus to Amazon Bedrock. In our previous posts, we explored general Cloud Financial Management principles on generative AI adoption and strategies for custom model development using Amazon EC2 and Amazon SageMaker AI. Today, we’ll guide you through cost optimization techniques for Amazon Bedrock, AWS’s fully managed service that provides access to leading foundation models. We’ll explore making informed decisions about pricing options, model selection, knowledge base optimization, prompt caching, and automated reasoning. Whether you’re just starting with foundation models or looking to optimize your existing Amazon Bedrock implementation, these techniques will help you balance capability and cost while leveraging the convenience of managed AI models.

Amazon EC2 and SageMaker AI are two of the foundational AWS services for Generative AI. Amazon EC2 provides the scalable computing power needed for training and inference, while SageMaker AI offers built-in tools for model development, deployment, and optimization. Cost optimization is crucial since Generative AI workloads require high-performance accelerators (GPU, Trainium, or Inferentia) and extensive processing, which can become expensive without efficient resource management. By leveraging the below cost optimization strategies, you can reduce costs while maintaining performance and scalability.

If you or your organizations are in the midst of exploring generative AI technologies, it’s important for you to be aware of the investment that comes with these advanced applications. While you are aiming at the expected return on your generative AI investment, such as, operational efficiency, increased productivity, or improved customer satisfaction, you should also have a good understanding of levers you can use to drive cost savings and enhanced efficiency. To guide you through this exciting journey, we will publish a series of blog posts filled with practical tips to help AI practitioners and FinOps leaders understand how to optimize the costs associated with your generative AI adoption with AWS.

In this article, we will show you how to define a chargeback mechanism that allocates Savings Plans purchased in the management account, linked accounts or both to recipient accounts of Savings Plan discounts. You can identify accounts that received Savings Plans discounts and the appropriate amount to chargeback to them based on their specific usage.

In this blog post, we discuss how Amazon used AWS Billing Conductor to build a custom solution, enabling them to view their AWS cost at internal rates in AWS Cost Explorer and AWS Cost and Usage Report (CUR).

With re:Invent 2024 in the books, and over 50 launch announcements, here are four that we’re most excited about. The overarching theme of these launches appears to be leveraging Amazon’s automation capabilities to optimize costs and improve efficiency for customers.

With great pleasure, I am happy to share with you the ten features recently added to the AWS Cloud Financial Management portfolio of services. We hope that these ten new features will help accomplish your daily FinOps tasks more effectively. These new features are like our holiday gifts to you. Enjoy your holiday and these special gifts from us. We look forward to hearing about your experiences with them.