Compare Statseeker vs. TrapStation

Statseeker is a powerful network performance monitor solution. It's fast, scalable, and cost-effective. Statseeker requires only one server or virtual machine to be up and running in minutes. It can also discover your entire network in under an hour without any significant impact on your bandwidth availability. It can monitor networks of all sizes, polling upto one million interfaces every sixty second, and collecting network data like SNMP, ping, NetFlow (sFlow, and J-Flow), sylog and trap messages, SDN configuration, and health metrics. Statseeker performance data are never averaged or rolled up. This eliminates the guesswork when it comes to identifying over- and underestimated infrastructure, root cause analysis, capacity planning, and other tasks. Statseeker's complete data retention means the in-built analytic engine can accurately detect anomalies in performance and forecast network behaviour months in advance. This allows network admins to plan and perform cost-effective, preventative maintenance, instead of fire-fighting problems as they occur. Statseeker's dashboards and out-of-the box reports allow you to troubleshoot and fix problems in your network before users are aware.

TrapStation logs and forwards SNMP SNMP traps, often to distributed management systems. You can route traps selectively, translate any SNMP version and filter, correlate, apply thresholds and modify varbinds. You can then view graphs, search logs and replay traps. TrapStation is a modern design that draws on decades of event-handling expertise. TrapStation was created to replace legacy apps like TrapEXPLODER and TrapBlaster. You can use TrapStation to access SNMP v3 encryption/security and trap modification, log search/replay and a browser interface. We hope TrapStation is a compelling alternative for unsupported scripts or in-house developers. TrapStation maps incomingtraps to your rule trees nodes. Each node has a filter that tests traps and allows you to log and forward match traps. The complexity of individual rule nodes is reduced by the formation of nested tiers, which are more specific. Nesting ensures that a partially matched trap is placed in a fail-safe rule Node.