Machine learning automatically catches critical problems. No figuring out what to monitor. No complex rule definitions. No expertise needed.
We reliably catch critical problems without human intervention and without alarm fatigue. To do this we use machine learning to auto-parse events, learn their patterns and find anomalies (events that break pattern). Our machine learning also looks for correlations across services so that it can identify the leading event(s) that caused the problem.
Detecting symptoms is not enough. We find the "leading edge" - the event that triggered the problem. Giving you more time to deal with it.
A common use case for monitoring is to alert when metrics reach undesirable thresholds. While this can be effective for identifying problem symptoms, it often means a problem won’t be noticed until it has already wreaked havoc. To avoid this, our machine learning detects the leading edge of problems rather than predefined symptom thresholds. During testing across 30+ application stacks, 56% of the time it successfully identified the events that occurred when the problem first happened.
We take you straight to the specific events and context needed to track down root cause. Our elegant user-interface makes resolution a breeze.
Our machine learning identifies and takes you to the specific events and context that relate to a problem's root cause. Our user interface lets quickly find what you need - with structured searching, easy filtering (by event, log source, event meta-data, etc.) and fast performance even for the largest data sets.
Warning - if you try these with <*>, you will be disappointed!
*Insert your favorite log manager or monitoring tool here.