Manage AI costs with Amazon Bedrock Projects

AWS has introduced Amazon Bedrock Projects, a new capability designed to solve the growing challenge of managing and attributing costs as organizations scale their generative AI workloads. The feature creates a logical boundary—a 'Project'—around specific workloads such as an application, environment, or experiment. To track costs, users attach resource tags (e.g., Application: CustomerChatbot, Team: DataScience) to a project and then pass the project's unique ID in their API calls to Bedrock's OpenAI-compatible APIs (Responses and Chat Completions). This tagged cost data is then activated in AWS Billing, making it available for detailed analysis.

Once configured, the cost attribution data becomes filterable and groupable within AWS Cost Explorer and AWS Data Exports. This enables practical financial operations previously difficult with AI spending: engineering teams can investigate sudden cost spikes tied to a specific model or application, finance departments can accurately perform chargebacks to different business units based on their AI usage, and platform teams can make data-driven optimization decisions by comparing the cost efficiency of different projects. The post outlines an end-to-end setup, from defining a tagging strategy with keys like Application, Environment, and CostCenter, to creating projects via a dedicated API and integrating the project ID into inference requests.