Nova Anomaly Detection uses AI to learn the normal behavior of every metric, log stream, and trace in your stack. When signals deviate from learned baselines, Nova fires early warnings hours before thresholds breach. Multi-signal correlation ties related anomalies together so you see the emerging incident, not a wall of isolated alerts.
Nova's anomaly detection engine continuously analyzes millions of data points across your infrastructure. Machine learning models trained on your specific traffic patterns, deployment schedules, and seasonal trends identify deviations that static thresholds miss. The system predicts issues up to 4 hours before they become user-facing incidents, giving your team time to act proactively.
Static thresholds break when traffic patterns change. Nova learns the unique baseline for every metric, accounting for day-of-week patterns, time-of-day cycles, deployment windows, and seasonal peaks. When Black Friday traffic doubles your normal load, Nova adjusts its baselines accordingly instead of flooding you with false alerts.
A CPU spike alone might be noise. But a CPU spike combined with increasing error rates, slower database queries, and a recent deployment? That is an emerging incident. Nova correlates anomalies across all signal types, metrics, logs, traces, and events, to surface the full picture. Instead of 47 separate alerts, you get one correlated anomaly cluster with root cause context.
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