Productivity AutomationVerified
Reviewed and published by trentmaziarz, July 11, 2026. Discovered and drafted by our automated research pipeline.
Dynatrace offers Davis AI predictive forecasting that analyzes historical resource usage data to predict when cloud infrastructure — such as disk space, CPU, or memory — is likely to run out, allowing teams to act before an outage occurs rather than reacting after the fact.
Details
Davis AI uses an AutoML (automated machine learning) approach that analyzes time series data stored in the Grail data lakehouse, detecting variance, seasonality, and trends to select the best forecasting model automatically. The system can predict resource consumption for thousands of individual components in parallel — for example, Dynatrace's own internal infrastructure tracks over 8,000 disks using this approach. For Kubernetes workloads, predictive AI can be combined with generative AI to automatically open pull requests on GitHub, suggesting scaling adjustments to manifest files for engineer review.
Products affected
Davis AIGrailDynatrace WorkflowsDynatrace Intelligence
Sources & Evidence
Company Disclosure
Other practices by Dynatrace
ProductivityDynatrace announced domain-specific AI agents in January 2026 that autonomously handle recurring operational tasks for site reliability engineers (SREs), developers, and security teams — such as investigating incidents, triaging vulnerabilities, and coordinating remediation across enterprise tools like ServiceNow, GitHub, and Jira.OtherDynatrace deployed Davis AI as part of its Application Security product to continuously scan running applications for software vulnerabilities in real time, automatically prioritize the most critical ones based on actual runtime exposure, and recommend remediation steps — reducing the volume of security alerts that security teams need to manually review.Data AnalysisDynatrace deployed Davis AI, an engine that automatically scans IT environments to detect performance anomalies and pinpoint their root cause — without a human having to manually dig through logs or alerts. When something goes wrong in a cloud application or infrastructure, the system identifies the precise source of the problem and notifies operations teams.
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