Logz.io has added AI and ML-powered Anomaly Detection capability to App 360, equipping its application observability solution with automated capabilities that let users respond to real-time performance alerts based on models built from historical telemetry data, reducing the manual tasks that slow down and complicate remediation.

“We continue to rapidly expand upon and deepen the capabilities of App 360, our groundbreaking application observability solution,” said Asaf Yigal, co-founder and CTO at Logz.io. “Anomaly Detection for App 360 is the kind of AI-driven automation that customers are asking for to help them optimize user experience while increasing efficiency and driving down costs. This added capability helps our customers find the ‘unknown unknowns’ lurking in their complex microservices architectures, cutting through the mountains of available data to focus on priority issues and troubleshoot faster.”

Anomaly Detection for App 360 utilizes powerful automation to make it simple for users to set up and begin monitoring and alerting against their critical services. Whether users prefer a list-based approach using Logz.io Service Overview or topology-based approach using Logz.io Service Map, the new capability also supports these varied use cases oriented to different audiences, including software engineers, SREs, platforming engineering and beyond. Anomaly Detection for App 360 takes users beyond traditional monitoring of critical services by locating and scoring the severity of unusual activity for a more proactive response.

Key Benefits of Anomaly Detection for App 360

• Faster troubleshooting: Increases app performance through automated detection. This new capability automatically surfaces emerging problems in the most critical services as designated by the user. Troubleshooting is accelerated by enabling the user to focus on those alerts that matter most to application optimization.
• Proactive and real-time: Proactively identifies issues that may otherwise go unnoticed. Advanced automation uncovers hard-to-predict issues before they impact end users. In contrast to traditional point-in-time, threshold-based application monitoring, Anomaly Detection enlists full-stack application observability that is more relevant and real-time.
• Automated insights: Moves away from traditional APM to full-scope application observability. Traditional APM solutions based on threshold-based detection typically require users to manually analyze available data, leaving users chasing high-volume alerts that may or may not be high priority. In contrast, Anomaly Detection for App 360 automatically generates real-time insights into the performance of user-prioritized services, operations, metrics and endpoints. This helps engineering teams accelerate and simplify their work in optimizing application performance, cutting through noise and reducing manual tasks.

For existing Logz.io customers, Anomaly Detection for App 360 is already available at no additional cost; it’s simply enabled as a new element of the platform.