Tokenomics at scale: How Jamf built real-time spend enforcement for Amazon Bedrock
As generative AI adoption scales, cost governance becomes a top challenge. Learn how Jamf built real-time, per-user spend enforcement for Amazon Bedrock using IAM Customer Managed Policies, an Amazon Athena cost view, and a serverless AWS Lambda loop that applies tiered model limits in near-real-time without disrupting active sessions.
Jamf, a company trusted by over 76,000 organizations for managing and securing Apple devices, encountered challenges while expanding AI access to its engineering organization through Amazon Bedrock. The company needed to understand its daily spend per engineer, set caps without slowing engineers, and determine if productivity gains justified the cost. To address these concerns, Jamf developed a real-time spend enforcement system for Amazon Bedrock, which tracks per-user visibility and cost accountability.
The solution utilizes AWS Identity and Access Management (IAM) Customer Managed Policies (CMPs), Amazon Athena-based cost views, and a serverless AWS Lambda enforcement loop. It does not block low-cost models but restricts access to higher-priced models when daily budget thresholds are approached. The architecture consists of three serverless components: measuring spending, deciding who to restrict and notifying them, and enforcing the restrictions.
These components work together in near-real-time, without disrupting active sessions, to enforce tiered spend limits.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
