![]() To manage performance and reliability effectively in modern, cloud-native environments, teams need AI on their side. Today, however, the nature of software environments has changed. When applications were deployed as monoliths on individual virtual machines, software environments were small enough in scale and simple enough architecturally for teams to manage them without the assistance of AI. In the past, it was easy enough to troubleshoot software performance or availability issues without the help of AI. ![]() In this blog post, we explain why AI-driven APM is so crucial for modern teams, then walk through the ways in which Splunk APM uses AI to enable easy, fast and scalable troubleshooting in even the most complex of cloud-native application environments. By using AI to help teams understand the root cause of complex problems and suggesting the fastest path to remediation, Splunk APM makes it possible to meet MTTR goals and avoid alert fatigue even for teams that must trace tens of thousands of requests per second. That's why AI is built into the core of Splunk APM (Application Performance Monitoring). Without AI, it's simply not feasible to address the types of issues IT, SRE and dev teams must manage today within the mean time to repair (MTTR) constraints they are expected to meet. In the face of these challenges, artificial intelligence has become a must-have feature for managing complex application performance or availability problems at scale. Today, IT and site reliability engineering (SRE) teams face pressure to remediate problems faster than ever, within environments that are larger than ever, while contending with architectures that are more complex than ever. ![]()
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