How and when to deliver AI at a fixed cost with AWS

This article explains how public sector organizations can deliver AI at fixed, predictable costs using AWS services.

• Deploy open-weight models on Amazon SageMaker AI endpoints for zero-marginal-cost inference with fixed hourly pricing
• Use Amazon Bedrock as a pay-per-use overflow path for capacity exhaustion and frontier models
• Implement a gateway layer that routes requests intelligently, tracks usage, and enforces spending limits
• Committed capacity delivers 7x faster time-to-first-token but handles lower concurrency than managed APIs
• Size capacity by acceptable latency concurrency, not total token volume, through empirical load testing
• Use cost allocation tags, AWS CloudTrail, and AWS Budgets for governance and financial attribution
• Pattern works best for predictable budgets, multi-team deployments, and 8B+ parameter models

This architecture enables public sector agencies to move AI from experimental pilots to production with budget certainty while maintaining flexibility through intelligent overflow routing.