Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch

Amazon SageMaker AI now provides detailed inference metrics and a SageMaker Insights dashboard in Amazon CloudWatch to monitor and debug generative AI inference endpoints. The dashboard supports both single-model endpoints (SME) and inference component (IC) endpoints, and provides over 100 metrics, including GPU health, token-level latency, and KV cache pressure. This allows machine learning platform engineers, MLOps teams, and site reliability engineers (SREs) to keep inference endpoints healthy, responsive, and cost-efficient. The practical implication for engineers building AI systems is that they can now easily monitor and troubleshoot their generative AI inference endpoints, reducing downtime and improving overall performance. The SageMaker Insights dashboard provides a fully managed observability solution, removing the need for custom Grafana dashboards and Prometheus configuration

⚡ Key Takeaways

• SageMaker endpoints emit over 100 detailed inference metrics, including GPU health, token-level latency, and KV cache pressure.
• The SageMaker Insights dashboard supports both single-model endpoints (SME) and inference component (IC) endpoints.
• The dashboard provides three views: Performance, Capacity, and Reliability, to monitor fleet health.
• Metrics can be connected to custom observability tools through a PromQL-compatible endpoint.
• Inference component (IC) endpoints are the recommended architecture for production generative AI workloads, supporting multi-model hosting on shared GPU infrastructure.

The ability to monitor and debug generative AI inference endpoints is crucial for ensuring the reliability and performance of AI systems in production. With SageMaker's detailed metrics and Insights dashboard, engineers can quickly identify and resolve issues, reducing downtime and improving overall system efficiency.

✅ Practical Steps

1. Turn on detailed observability metrics on new and existing SageMaker inference endpoints.
2. Navigate the SageMaker Insights dashboard to monitor fleet health across Performance, Capacity, and Reliability views.
3. Connect the metrics to your own observability tool (Grafana, Datadog) through the PromQL-compatible endpoint.

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