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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 16 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.

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.

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.