AWS Cloud Financial Management

In response to customer requests for a more consistent cost savings experience and broader set of recommendations, AWS Trusted Advisor is expanding its capabilities. We’re excited to announce the integration of 15 new checks from AWS Cost Optimization Hub into Trusted Advisor. This significant update provides more actionable insights to help you optimize your AWS spend.

As organizations scale cloud usage, understanding cost variations becomes increasingly complex. Many of you have told us that you sometimes had to spend hours analyzing why costs changed from one month to another. To address this, we’re excited to announce a new cost comparison feature in AWS Cost Explorer that provides automated month-over-month cost change analysis. This feature enables you to quickly identify, understand, and explain variations in your AWS spending. With this new feature, you can now pinpoint the largest cost changes across any cost dimension, such as services, accounts and regions, and drill down into detailed explanations of these changes, including shifts in usage patterns, changes in commitment-based discounts, and applied credits within seconds.

Today, we’re excited to announce the general availability of the authenticated AWS Pricing Calculator in the AWS Billing and Cost Management Console. The new capability improves the accuracy of cost estimates for new workloads or modifications to your existing AWS usage by incorporating eligible discounts and commitment savings. You can now easily model cost changes for things such as migrating workloads between regions, modifying existing or planning new workloads, and planning for commitment purchases.

Starting today, AWS Compute Optimizer delivers new recommendations for your Amazon Aurora DB clusters. Compute Optimizer analyzes the cost of your clusters and identifies opportunities to leverage Aurora I/O-Optimized cluster storage configuration to save cost and improve price predictability for your most I/O-intensive workloads.

While San Diego’s famous June gloom may have given FinOps attendees overcast skies, the energy inside the FinOps X conference was anything but gloomy. Within just 1.5 days, we engaged in many insightful conversations. As a return, we have brought with us several feature enhancements that will hopefully bring more sunshine to your day-to-day FinOps life.

We’re excited to engage with the FinOps community at FinOps X 2025 in San Diego, June 2-5. If you’re looking to spend quality time with the AWS team, here’s where you can find us from reception parties on Monday evening, keynote, breakout sessions, booth, and customer meetings. Can’t wait to see you all soon!

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.