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Amazon SageMaker AI Announces New Observability Capability for Inference Endpoints

Amazon SageMaker AI provides new observability capabilities for inference endpoints, enabling real-time monitoring of token performance, GPU health, and infrastructure status to quickly diagnose and resolve issues.

Amazon SageMaker AI introduces new observability capabilities that allow customers to confidently run production generative AI inference workloads by providing comprehensive visibility into token performance, GPU health, inference component placement, and autoscaling behavior. This eliminates the manual effort of searching CloudWatch for per-endpoint metrics, correlating latency spikes with GPU saturation or KV cache exhaustion, and diagnosing slow scaling operations. The capability tracks inference performance metrics in real-time, including Time to First Token, inter-token latency, queue depth, and tokens per second, and surfaces them alongside infrastructure health. SageMaker AI's pre-built Insights dashboard in Amazon CloudWatch provides token latency, GPU utilization, inference component copy counts, scaling events, and cold start breakdowns in a single view with OpenTelemetry native metrics published automatically. This allows teams to quickly diagnose TTFT degradation, verify availability zone compliance, and tune autoscaling policies. Customers standardized on observability tools like Grafana can connect directly using the regional PromQL endpoint and import a pre-configured dashboard template.

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