AWS Cloud Financial Management

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.

Today, we are excited to announce custom billing views, a new feature within AWS Billing and Cost Management that allows you to grant member accounts in your organization access to cost and usage view spanning multiple member accounts. Many of you have teams that own multiple AWS accounts and told us that you want to have a single view of cost data for each team. At the same time, you want to minimize the number of people who have access to the management account that owns the organization-level cost data. With the newly launched custom billing views, you can now make cost and usage data spanning multiple member accounts available to a designated member account in your organization. Let’s dive into how you can set this up.

Today, AWS announced Invoice Configuration, which provides you the ability to customize your invoices to fit your unique business needs. Invoice Configuration enables you to receive separate AWS invoices for each of your business entities such as subsidiaries, cost centers, legal entities, departments etc., while being a part of the same AWS Organization.

Invoice Configuration enables you to split AWS charges on a business entity level, designate a separate Invoice Receiver, and receive separate invoices for each of your business entities. This not only enables you to process your AWS Invoices faster, but also enables you to track funding for each business entity separately and enables you to customize your AWS Invoices to adhere with unique FinOps processes that you may have across your business entities.