Cost tracking multi-tenant model inference on Amazon Bedrock

This article discusses a comprehensive solution for tracking and managing multi-tenant model inference costs on Amazon Bedrock, focusing on how organizations can gain detailed insights into AI application usage.

• Uses Converse API's requestMetadata parameter to add tenant-specific identifiers to model requests
• Implements an ETL pipeline using AWS Glue to process and transform Bedrock invocation logs
• Leverages Amazon QuickSight for creating tenant-based analytics dashboards
• Enables granular cost tracking and performance analysis across different customer segments
• Provides a scalable approach to understanding AI model usage and optimizing resource allocation

The solution transforms basic model logs into strategic insights, helping businesses make data-driven decisions about their AI applications and optimize cost management across multiple tenants.