Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality

This article explains how to implement comprehensive observability for LLM inference on Amazon SageMaker AI, covering both infrastructure and output quality monitoring.

• Monitor quantity: GPU utilization, latency, invocations, and cost attribution per model
• Monitor quality: composite scores, safety, relevance, and tone across LLM responses
• Use SageMaker AI enhanced metrics for automatic infrastructure visibility
• Publish custom quality metrics to CloudWatch for LLM output evaluation
• Build Grafana dashboards combining both dimensions for unified observability
• Implement threshold-based alerts routed to SNS for SRE triage
• Use LLM-as-judge pattern with Bedrock for quality score computation
• Sample notebooks available in AWS GitHub repository for implementation

Production-grade LLM observability requires monitoring both operational health and output quality together, enabling cost optimization and quality assurance across multi-model endpoints.