live metricsstreaming
Req/s12,418
p95184ms
Errors0.04%
ML models learn the normal behavior of your signals and flag drift, using peer comparison and adaptive baselines rather than a single static threshold.
Adaptive baselines
The detector models normal behavior per signal instead of relying on one fixed threshold for everything.
Peer comparison
Compares a node against similar peers to catch deviations a lone threshold would miss.
Early warning
Built to catch slow degradations that creep below the radar of threshold alerts.
Capabilities
Everything you need, nothing you don't.
- Adaptive, per-signal baselines
- Peer-comparison detection
- Trained on your topology, not a generic model
Explore the platform
Related capabilities
Ready to see what fails first?
Start modeling your infrastructure today, or talk to our team about an enterprise rollout.