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The ability to group and analyze costs across resources and accounts is crucial for gaining visibility, identifying optimization opportunities, and making data-driven decisions. Organizations aim to accurately allocate and track cloud costs across different business units, projects, and environments to improve budgeting, enable effective chargeback processes, and make informed optimization decisions. With AWS Cost Categories, a free feature, you can create rules to flexibly group and visualize cost using various dimensions such as account, charge type, service and even other Cost Categories.

In this first part of our two-part series, we will explore the fundamentals of AWS Cost Categories and demonstrate how they can transform your cost management approach.

Amazon Q Developer CLI (Amazon Q CLI) with the Model Context Protocol (MCP) for Billing and Cost Management and Amazon CloudWatch enables teams to quickly generate detailed cost analyses, perform deep dives into usage patterns, and receive optimization recommendations through a streamlined interface. Amazon Q CLI with MCP servers simplifies CloudWatch cost analysis. Using natural language prompts, you can identify cost drivers and receive optimization recommendations for your resources. This streamlined approach minimizes manual exploration, enhances financial control, and improves cloud efficiency.

AWS is committed to empowering the FinOps community with the knowledge, tools, and best practices needed to excel in cloud financial management. Our dedication to the FinOps community goes beyond providing world-class cloud services—we’re invested in your professional growth and success. Through our comprehensive digital training portfolio, we aim to equip FinOps professionals with the expertise needed to drive meaningful cost optimization, establish effective governance frameworks, and demonstrate clear business value from cloud investments. Today, we’re excited to share updates to our Cloud Financial Management training courses and introduce a specialized new course designed specifically for the growing demands of generative AI cost management.

The decision between Containers and Serverless Functions – or the implementation of both – should be driven by a thorough understanding of workload characteristics, cost implications, and operational requirements. As FinOps professionals, you should work closely with development and operations teams to analyze usage patterns, model costs under different scenarios, and consider factors like development velocity, operational overhead, and long-term maintainability. By leveraging the strengths of both Containers and Serverless technologies, you can build flexible, cost-effective cloud architectures that adapt to changing business needs while optimizing resource utilization and expenditure.

A cloud vendor’s Cloud Financial Management (CFM) capabilities are crucial for your success in the cloud. Whether you’re planning future investments, optimizing current spending, or allocating costs across your organization, having the right CFM tools makes all the difference. AWS is proud to be recognized with the 1st place in forecasting and estimation use case, and top 3 in driving cost efficiency use case and promotion accountability use case in the latest 2025 Gartner Critical Capabilities for Cloud Financial Management Tools research.

Today, we are excited to announce new capabilities within AWS Billing and Cost Management that enable you to create custom billing views containing cost and usage data from multiple organizations. You can now share custom billing views with AWS accounts outside your organization and combine multiple custom billing views to create consolidated multi-source views. These features allow you to access cost management data across multiple organizations through AWS Cost Explorer and AWS Budgets from a single AWS account.

Accurately allocating cloud costs in AWS is essential for fostering accountability and maximizing the value derived from cloud investments. Cost allocation requires careful consideration of your organization’s structure, workload patterns, and financial requirements. Whether you’re utilizing AWS accounts, Cost Allocation Tags, Cost Categories, or Billing Conductor, the key is selecting patterns that align with your business needs while maintaining simplicity and scalability. Start with the fundamental building blocks of AWS cost allocation, then layer in more sophisticated approaches as your organization’s needs evolve. By implementing these prescriptive patterns thoughtfully, you can create the cost transparency and accountability needed to drive business value from your cloud investments. Remember that cost allocation is not a one-time exercise—regularly review and adjust your approach as your business grows and your cloud journey continues.

Savings Plans offer a flexible pricing model that provides you up to 72% savings on your AWS compute workload compared to on-demand prices. When your AWS footprint grows through organic expansion, regional scaling, mergers, acquisitions, or setting up AWS Organizations to align with your business requirements, understanding how the recommendations are made at payer and linked account levels helps you manage Savings Plans at scale. If you are managing a multi-account AWS Organization, you might have noticed that Savings Plan recommendations differ between your payer account (also known as management account) and linked account (also known as member account).

In this blog, we’ll explore a straightforward, automated approach to departmental cost allocation using AWS Organizations and AWS Lambda. The solution eliminates manual tracking and gives you clear visibility into departmental spending.

We are excited to release the aws-pricing-mcp-server, an open-source tool in the AWS Labs GitHub repository, that brings natural language pricing queries to your favorite AI assistants through the Model Context Protocol (MCP). Now you can simply ask “What would it cost to run three m5.large instances and a MySQL RDS database in us-west-2?” and get instant pricing answers using natural language queries without leaving your workflow.