We are running a pilot of AWS DevOps Guru paired with Amazon Q across a federal AWS estate.
DevOps Guru provides ML-driven anomaly detection and automated root cause analysis. Rather than relying on manually defined alert thresholds, it builds a baseline from operational data and flags deviations — reducing noise and surfacing issues that threshold-based alerting misses.
Amazon Q brings generative AI into engineer troubleshooting workflows. When an anomaly is flagged, engineers can query Amazon Q directly for accelerated diagnosis — pulling in relevant runbooks, log context, and suggested remediation paths without switching tools.
Benchmark against Datadog Watchdog — the pilot includes a structured head-to-head comparison against Datadog Watchdog across quantified cost and security scenarios. Evaluation criteria include detection accuracy, time-to-diagnosis, alert fatigue, and total cost of ownership.
Results from the benchmark will inform a longer-term AIOps tooling decision for the environment.