Static thresholds can't keep up with dynamic environments
In cloud-native systems, normal changes by the minute. Dynatrace AI keeps up automatically, alerting only when something actually matters.
No manual threshold
configurationDynatrace autogenerates baselines for every component automatically and adjusts them as your environment changes.
Alerts that reflect customer
impactNot every anomaly is serious. Dynatrace AI determines whether a deviation has actual or potential impact on customers before notifying your team.
Built for dynamic
environmentsContainers, microservices, and multi-cloud architectures are handled natively, with no static rules to maintain.
Built to detect the right things at the right time
From dynamic baselining to predictive analytics, every detection is grounded in how your system actually behaves.
Anomaly detection built for cloud-native complexity
Traditional static threshold alerting breaks down in environments where normal changes constantly. Dynatrace uses real-time topology awareness, multidimensional baselining, and dynamic dependency detection to identify anomalies based on what your system is doing, not what someone configured months ago.
Focus on problems that affect customers, not alert noise
Dynatrace AI automatically classifies anomalies by severity and customer impact, so your team sees fewer, more meaningful notifications. Problem detection is based on 100% of customer transactions, not averages or samples, so nothing that matters gets missed.
Detect anomalies and predict problems before they occur
Dynatrace learns your application traffic patterns and raises alerts when a statistically relevant deviation is detected, including a quantified customer impact and root cause insight. Predictive analytics also forecast upcoming traffic levels and seasonal patterns, catching issues before they escalate.

With Dynatrace, we have shortened the time to identify and solve performance problems by 60%, and have achieved 100% application performance visualization.
Find out what smarter anomaly detection looks like






