Nova's ML models learn your system's normal behavior, across every metric, service, and time-of-day pattern, and detect deviations while they're still recoverable. Stop reacting to outages. Start preventing them.
Static alert thresholds break the moment your traffic pattern changes, Black Friday, a marketing campaign, a new release. Nova's ML models continuously learn what "normal" looks like for each service, each metric, and each hour of the week. A Tuesday at 3pm has a different baseline than a Friday at 6pm, and Nova knows the difference.
Nova projects forward-looking forecasts for capacity-related metrics: disk utilization, memory, connection pool usage, and queue depth. When Nova predicts disk will hit 95% in 6 hours, you get the early warning now, with time to either clean up data or provision capacity before any user notices.
Nova scores every anomaly on a 0–100 confidence scale across multiple dimensions simultaneously: magnitude of deviation, rate of change, duration, and blast radius. A latency spike that's also accompanied by error rate increases and drops in throughput scores much higher than an isolated CPU blip on a non-critical host.
Early warnings from Nova include everything your engineer needs to act: the affected service, the metric that deviated, the predicted trajectory, similar past incidents with their resolutions, and a suggested runbook. Engineers don't spend time figuring out what they're looking at, they spend time fixing it.
ML-powered baseline learning that adapts to your traffic patterns. No manual threshold tuning required.