Optimize LLM Costs on Amazon Bedrock: From Billing Attribution to Operational Telemetry

This article presents a three-layer observability framework for optimizing LLM costs on Amazon Bedrock through billing attribution, model invocation logging, and OpenTelemetry instrumentation.

• Layer 1: Enable IAM-based cost allocation and native CloudWatch metrics for per-user and per-team billing visibility
• Layer 2: Implement model invocation logging to Amazon S3 or CloudWatch Logs to capture full API call details and usage patterns
• Layer 3: Deploy OpenTelemetry instrumentation in client applications for operational context and per-developer attribution
• Five cost optimization levers: model switching (30-50% savings), cache efficiency (up to 90% savings), error-driven waste reduction, tool optimization, and per-developer accountability
• Phased implementation approach: Week 1 for Layer 1, Weeks 2-3 for Layer 2, Weeks 3-4 for Layer 3
• Complementary strategies include Intelligent Prompt Routing, Model Distillation, batch inference, and Reserved Tier for additional cost reductions

The framework enables organizations to move beyond infrastructure-level billing data to operational insights that drive informed LLM cost optimization decisions across teams and workloads.