Sosivio is a predictive troubleshooting platform built specifically for Kubernetes environments and applications. By leveraging the use of state-of-the-art AI/ML, Sosivio eliminates ambiguity in the troubleshooting process by filtering out all the irrelevant information, only providing the insights and answers for DevOps, IT, Developer, and Security teams. Sosivio passively observes cloud-native environments to find the root cause of issues proactively AND provides a recommended fix for critical failures and future failures. Meaningful and actionable analysis and insights start with good data. Sosivio’s proprietary granular data collectors look at multiple layers of the infrastructure stack including the kernel, operating system, processes, network, container runtime, application logs, and Kubernetes layer. This granular data is fed through multiple AI/ML engines to determine the root cause of issues before they occur and provide instant remediation via a recommended command line fix. Sosivio’s non-intrusive architecture operates with any Kubernetes flavor as well as on-prem in disconnected and air-gapped environments perfectly suited for highly regulated industries. There is no data-offloading and no persistent storage as all the data collection and analysis are done on-the-fly in real-time driven by Sosivio’s Data Swirling architecture. The same Data Swirling AI/ML engines enable a broader suite of features including Application Profiling Insights and Recommendations which analyze and profile applications usage to provide an optimized recommendation for resource allocation improving performance and reducing cloud spend. And, included in Sosivio’s always free Community version are Kubernetes component health checks and ultra-granular real-time metrics with features like SDN latency checks, visibility into context switches and network connections, and much more. To learn more, follow Sosivio on LinkedIn or visit www.sosiv.io