Implementing per-user token guardrails for Amazon Bedrock in government agencies

This article presents two complementary patterns for implementing per-user token guardrails on Amazon Bedrock to help government agencies control generative AI spending while maintaining governance compliance with OMB mandates.

• Pattern 1 uses CloudWatch logging, Lambda, and DynamoDB to monitor and enforce token limits on Claude Code CLI/developer access
• Pattern 2 embeds enforcement in application-level LLM service layers for custom applications with pre-invocation limit checks
• Both patterns use a shared data model with default token limits per model and per-user overrides for higher allocations
• Enforcement options include IAM deny policies (hard block) or gateway checks (soft block) with custom error messages
• Daily reset functions restore access at midnight UTC; alerts notify users at 80% and 100% consumption thresholds
• Admin dashboards provide agency-wide visibility into token consumption by user, model, and time period
• Security considerations include least-privilege IAM roles, tamper-resistant limit records, audit trails, and encryption at rest

These modular patterns enable agencies to accelerate AI adoption responsibly while preventing runaway token consumption and maintaining audit compliance.