Getting hit with alarms every time some threshold is violated? Dynatrace consolidates all related performance issues into a single actionable notification. Less noise, more problem-solving.
Not every threshold violation is a problem, and not all problems are created equal. Artificial intelligence determines whether an anomaly has an actual or potential impact on customers.
Dynatrace integrates multidimensional baselining and predictive analytics to automatically detect when things aren’t behaving as expected. Get notified only when something needs your attention.
The traditional reactive approach of identifying problems by responding to alerts based on static thresholds doesn’t work for today’s elastic cloud infrastructure, containers, and microservices. With so may components in perpetual motion and “normal” behavior constantly being redefined, these dynamic environments demand a new, proactive approach.
That’s where artificial intelligence comes in. With deepest possible knowledge of your system’s topology, dynamic baseline performance, and behavior, Dynatrace harnesses predictive analytics and continuous machine learning to auto-identify anomalies based on the metrics that matter for your particular environment.
“If we get an alert, we immediately know what to do and how to fix the issue. Days of spammed inboxes are a thing of the past!”
“Dynatrace is obviously designed to help identify problems rather than overwhelm us with metrics.”
“With Dynatrace, we have shortened the time to identify and solve performance problems by 60%.”
Identify performance anomalies before they affect customers. Eliminate guesswork and stop spending time hunting down problems. Dynatrace automatically applies AI algorithms to determine whether a performance issue has an actual or potential impact on customers.
Because the AI-powered anomaly detection engine understands the relationship between operational and business metrics, you get a single notification only when something impacts customers’ user experience.
Dynatrace uses different methodologies to determine when anomalous behavior warrants a problem notification. Automatic multidimensional baselining detects violations of individual reference values that change over time (response times and error rates of application or services). Predictive analytics detect abnormalities in application traffic and service load—as traffic and load depend on business-model seasonal patterns (e.g., workweek vs. weekends, day vs. night, Black Friday).