Anomaly detection continuously watches your systems and flags unusual patterns or deviations from normal behavior — the early signal that something is going wrong, often well before a static alert would fire.

Static thresholds require you to guess a limit for every metric and fire whenever it’s crossed — noisy during expected peaks and blind to problems below the line. Anomaly detection learns what’s normal for each signal, including seasonality, and flags genuine deviations instead.

It builds context-aware baselines per signal that understand daily, weekly, and seasonal patterns, scores each deviation by severity, and suppresses benign fluctuations — then sharpens further as your team confirms or dismisses anomalies.

Metrics, logs, and traces across your whole estate — high-cardinality time series, log streams, and distributed traces — watched together so multivariate anomalies surface, not just single-metric spikes.

Yes. It learns recurring daily, weekly, and seasonal rhythms and separates trend from anomaly, so predictable peaks don’t trigger alerts and real deviations still stand out.

High-confidence anomalies are scored, ranked, and routed to the right team — and can feed straight into AI-powered root cause analysis and automated remediation to move from detection to resolution.