Get Started

Detect what rule-based alerts miss — before your customers do.

Anomaly Radar

Deploys unsupervised ML models across infrastructure metrics to detect unusual patterns, cascading failures, and performance anomalies before they escalate into customer-impacting incidents. Reduces mean-time-to-detect (MTTD) for infrastructure incidents from hours to minutes in complex microservices environments.

Why It Matters

AnomalyRadar catches the incidents rule-based alerting was never built to see — the subtle degradations and cascading failures that hide below every static threshold until they hit customers.
Unsupervised ML learns each environment’s normal behavior and flags what deviates, correlating across thousands of metrics no human alert rule could cover.
1.png

MTTD from hours to minutes

In complex microservices environments, the gap between anomaly onset and customer impact is where incidents are won or lost — early detection turns outages into near-misses.

2.png

Finds the unknown unknowns

Rules only catch failures someone predicted — unsupervised detection surfaces novel patterns, cascading interactions, and slow degradations no one wrote a threshold for.

3.png

Correlation instead of noise

Thousands of metrics monitored simultaneously with anomalies correlated into coherent signals — where equivalent rule coverage would bury teams in unmanageable alert floods.

The Cloudly Advantage

AnomalyRadar is InfraPredict’s operational twin — one predicts capacity needs, the other detects live failures — together forming a complete AIOps intelligence pair on the same monitoring foundation we already sell.
1.png

Completes the AIOps pair

Prediction (InfraPredict) plus detection (AnomalyRadar) is the full proactive-operations story — the strongest expression of Cloudly's AIOps positioning as bundled products.

2.png

MTTD as measurable proof

Detection-time improvement shows up in the client's own incident history within weeks — fast, undeniable evidence that renews and expands contracts.

3.png

Feeds AlertOrchestrator naturally

ML-detected anomalies routed through intelligent alerting is the detect-route-respond loop as a pure product bundle — three products, one operations transformation.

4.png

Sharpens our managed services edge

Running AnomalyRadar inside our own 24/7 operations lets Cloudly detect client incidents faster than in-house teams — a service-quality moat competitors must build ML to match.

5.png

DevOps Guru pull-through

AWS DevOps Guru and Grafana ML integration deepens AWS Monitoring engagements in every deployment.

The Final Takeaway

Universal prospect base — Every microservices-running enterprise qualifies — with telecom NOCs and banking platform teams, our home verticals, suffering the worst alert noise today.

Powered By

Isolation Forest

Core to the AnomalyRadar technology stack.

LSTM autoencoders

Core to the AnomalyRadar technology stack.

AWS DevOps Guru

Core to the AnomalyRadar technology stack.

Grafana Machine Learning

Core to the AnomalyRadar technology stack.

Let's start a quick, free consultation

Get Started