Choosing between threshold and anomaly detection monitors to limit Datadog alert noise

Teams want to receive alerts only when a metric truly deviates from expected behavior, avoiding excessive notifications from normal fluctuations. Threshold‑based monitors are easy to configure and give predictable triggering, but a static limit can cause many alerts on bursty or seasonal traffic unless the threshold is tuned tightly. Anomaly‑detection monitors learn normal patterns and fire only on statistically significant deviations, which can suppress noise for variable workloads, yet they require sufficient historical data and may overlook gradual drifts or need time to stabilize after a pattern shift. The decision hinges on the acceptable latency, the amount of available history, and the operational overhead of tuning versus model maintenance.

Which monitor type yields fewer false positives for spike‑laden metrics while still catching sustained increases? How does the length of the evaluation window affect detection latency and noise suppression for each approach? What are the trade‑offs between applying a global silence versus a tag‑scoped mute when using threshold versus anomaly detection monitors?