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Agents1 min read

Real-time spend enforcement for Amazon Bedrock with IAM policies and Lambda

Jamf implemented real-time, per-user cost enforcement for Amazon Bedrock using IAM policies, Athena, and Lambda, enabling tiered model limits without disrupting sessions.

By OpenSmartRoute editorial · written through the router by llm-onprem

From AWS machine learning blog - “Tokenomics at scale: How Jamf built real-time spend enforcement for Amazon Bedrock

Serverless flow for enforcing per-user Amazon Bedrock spend: Bedrock logs to Amazon S3, Athena computes daily spend, and a scheduled Lambda updates IAM policies
Serverless flow for enforcing per-user Amazon Bedrock spend: Bedrock logs to Amazon S3, Athena computes daily spend, and a scheduled Lambda updates IAM policies. Image: AWS machine learning blog (original)

As generative AI adoption increases, managing costs becomes a significant challenge. Jamf addressed this by building a system for real-time, per-user spend enforcement on Amazon Bedrock.

The solution uses IAM Customer Managed Policies to control access, an Amazon Athena cost view to monitor expenses, and a serverless AWS Lambda loop to apply tiered model limits. This setup enforces spending policies in near-real-time while maintaining active sessions.

This approach allows organizations to govern AI model usage effectively at scale, ensuring cost control without impacting user experience or session continuity.

Source: https://aws.amazon.com/blogs/machine-learning/tokenomics-at-scale-how-jamf-built-real-time-spend-enforcement-for-amazon-bedrock/

Published Sep 1, 2026 · updated Sep 7, 2026 · 90 words

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