Build a proactive AI cost management system for Amazon Bedrock – Part 2
Machine Learning Blog
This article provides a detailed guide to building a proactive AI cost management system for Amazon Bedrock, focusing on advanced cost monitoring and tracking strategies.
- Introduces invocation-level tagging for enhanced traceability of AI requests
- Develops a new API input structure supporting custom tagging with optional parameters
- Creates custom CloudWatch metrics to track AI service usage across dimensions like model, cost center, and environment
- Leverages Amazon Bedrock's new application inference profiles for precise cost allocation
- Demonstrates how to use AWS Cost Explorer to generate detailed cost reports by tags
The solution combines real-time alerts and comprehensive cost reporting to provide a proactive approach to managing generative AI expenses, enabling organizations to control and understand their AI service spending.
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