IBM SevOne Network Performance Management is monitoring and analytics software that provides real-time visibility and insights into complex networks.
Vendor
IBM
Company Website




Application-centric network observability
Proactively manage network performance issues to ensure consistent service delivery and enhance business agility.
IBM® SevOne® provides app-centric observability and actionable insights to prevent costly disruptions in network performance. Designed for modern, complex networks across hybrid and multi-cloud environments, IBM® SevOne® leverages machine learning to transform raw data into proactive insights. With a focus on agility, reliability, and operational efficiency, it empowers NetOps teams to manage performance proactively, make data-driven decisions, and ensure a seamless user experience. Application-centric network observability
Features
- **Network insights: **Leverage machine learning to automatically understand what is normal and what’s not across your entire network.
- **App-centric insights: **Start speaking “app” to your network performance data and understand your network from an application perspective.
- **Automated actions: **Do more with less by leveraging enhanced tooling and automation.
- **ITOps integration: **Quickly spot, assess, and resolve issues before they impact your customers with proactive, real-time alerts integrated with leading AIOps and ITSM platforms.
Benefits
- **Go beyond detection: **Surface the insights that matter most and avoid costly performance issues.
- **Enable next-gen networks: **Enhance visibility to monitor SDN, SD-WAN, cloud and wifi networks across all environments.
- **Enhance user experience: **Know exactly where problems originate for faster resolution
- **Turn insights into actions: **Automatically take actions that help the continuous performance of your network.
- **Quickly understand “Is it the app or the network?”: **Enhance your collaboration with application teams when troubleshooting.
- **Early detection of anomalies: **Gain early warnings of developing network performance issues with machine learning-based insights