Augtera’s Network AI is purpose-built for today’s Hybrid IT challenges. Cloud-native, geo-diverse, and horizontally scalable with a simple per device license structure, makes Network AI the ideal, next-generation replacement for SolarWinds patchwork of poorly integrated acquisitions. Collect any data source at scale, use AI/ML to learn patterns and discover anomalies, prevent future incidents, reduce redundant alerts/tickets, identify incident root, create trouble tickets, use your own python automation playbooks, and more.
The table below illustrates some of the significant differences between a legacy tool and a forward-looking platform.
| Capability | Network AI | SolarWinds | Comment |
|---|---|---|---|
| Configurable dashboards | Yes | Yes | With widgets |
| Dashboard export in HTML | Yes | Yes | For third-party integration |
| Ticketing | Yes | Yes | Example Service Now. |
| SNMP, Flow data, Syslog | Yes | Yes | Performance limitations for SolarWinds |
| Static Topology Ingestion | Yes | Yes | – |
| Multi-vendor support | Yes | Yes | Arista, Cisco, Juniper, Dell, F5, VMware, other |
| JSON logs, Streaming Telemetry, Kafka, platform API | Yes | No | Industry directions & Integration |
| On-Prem | Yes | Yes | Good for rare Enterprises with acute operations data concerns |
| SaaS | Yes | No | SaaS relieves network tooling management burden from Network Operations Team |
| ML-based anomaly detection | Yes | No | Learns patterns rather than using thresholds Reduces false positives. Eliminates threshold maintenance burden |
| Multi-layer auto-correlation | Yes | No | Eliminates redundant alerts / tickets Quickly identifies incident root |
| Application Flow and Underlay Correlation | Yes | No | Reduces time to innocence & arguing with application teams about performance issues |
| High-frequency probes | Yes | No | Reduces identification time for latency and other probe metrics |
| Real-time rare log message identification | Yes | No | Allows Network Operations teams to prevent incidents before they occur |
| Real-time rate-based anomaly detection | Yes | No | Change of log message rate, for example bursts, indicate anomalies |
| Real-time Classification | Yes | No | Network operations teams can take action based on any log signature |
| Real-time Metric extraction | Yes | No | Metrics embedded in log messages can be placed in time-series, analyzed, and use real-time anomaly detection algorithms |
| Cloud-native Software Architecture | Yes | No | Align with broader IT software directions Take advantage of agile and scalable micro-services |
| Geo-diverse active-active Software Architecture | Yes | ? | High-availability whether due to software failure or software upgrade |
To learn more about Augtera’s Network AI:
