Description
Azure Monitor supports dynamic thresholds that use machine learning to learn seasonal patterns and trends from historical telemetry. This helps teams detect significant deviations (spikes, drops, unexpected shifts) without constantly tuning static thresholds. Dynamic thresholds are useful for noisy metrics and environments where normal behavior changes over time.
Details
- Pricing model: paid
- License: cloud_service
- API: Available · Docs
Alternatives
More tools from Anomaly Detection & Alerts.
ServiceNow ITOM (AIOps)
IT operations management suite that uses AIOps to detect anomalies, correlate events, and automate response.
IBM Cloud Pak for AIOps
Enterprise AIOps platform for event intelligence, anomaly detection, and incident automation across tools.
Anodot
AI-powered anomaly detection for business and operational metrics with automated root-cause insights.
Instana
Application performance monitoring with automated anomaly detection across services, traces, and infrastructure.
Amazon CloudWatch Outlier Detection
Built-in anomaly (outlier) detection for AWS CloudWatch metrics to create adaptive baselines and alarms.
Chronosphere
Cloud-native observability platform for large-scale metrics with strong alerting and operational insights.