MonitoringPaid

DATADOG

Cloud-scale observability and monitoring platform

ABOUT

AI and ML teams running production models face a visibility problem: model inference happens across distributed infrastructure (GPU clusters, container orchestration, serverless functions), and traditional monitoring tools weren't designed to track ML-specific metrics like inference latency, token throughput, GPU utilization, and model drift alongside standard infrastructure health. Datadog solves this by providing a unified observability platform that correlates ML model performance metrics with the underlying infrastructure, enabling teams to quickly diagnose whether a spike in inference latency is caused by model changes, infrastructure bottlenecks, or data pipeline issues — all in a single dashboard with built-in alerting and automation.

INSTALL

pip install datadog

INTEGRATION GUIDE

1. Monitor GPU utilization, memory usage, and inference latency across your model serving infrastructure in real-time dashboards 2. Correlate LLM response times and token throughput with underlying compute resource metrics to optimize model deployment costs 3. Set up alerts for model performance degradation that trigger automated rollbacks or auto-scaling of inference endpoints 4. Trace requests end-to-end through your AI pipeline — from user query through embedding generation to LLM inference and response 5. Aggregate and search model logs, input prompts, and output responses alongside infrastructure telemetry for unified debugging

DATADOG

ABOUT

INTEGRATION GUIDE

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