Part 2: Amazon Bedrock cost attribution with Amazon Athena and CUDOS

Understanding where your AI spend goes is now much clearer, offering direct insights into project profitability and resource allocation. This piece from AWS Machine Learning dives into making Amazon Bedrock costs transparent, showing how to attribute expenses precisely using Amazon Athena and the CUDOS dashboard. It details the setup of CUR 2.0 with IAM principal data, enabling users to query Bedrock spend by individual principal, specific project, or entire team, and then build visual dashboards to track these AI costs across an organization. For a mid-sized e-commerce platform based in Chicago, this means moving beyond a lump sum cloud bill. They can now definitively see that their personalized product recommendation engine, powered by Bedrock, consumed $X last month, while their customer service chatbot utilized $Y. This allows their finance team to accurately allocate costs to specific business units, informing strategic decisions about which AI initiatives offer the best return on investment. Similarly, for an independent SaaS founder in Denver, building a generative AI feature, the ability to monitor Bedrock usage per user or feature iteration helps them optimize their pricing model or identify where to streamline their prompt engineering efforts to reduce inference costs. An internal IT team at a manufacturing company in Detroit can assign Bedrock usage to R&D for their predictive maintenance models versus their supply chain optimization tools, ensuring departments are accountable for their computational footprint and driving efficient resource use. This granular visibility empowers organizations to optimize their AI investments, ensuring every dollar spent contributes meaningfully to business objectives. To put this into action tonight, consider identifying one internal project currently using Amazon Bedrock. Then, sketch out a simple structure for how you would ideally track its AI compute costs if you had perfect visibility. This mental exercise will highlight the specific data points you'd need and reveal the immediate benefits of implementing a similar attribution system, setting the stage for deeper exploration.