Let machine learning catch incidents and find root cause
Autonomous incident detection
Unsupervised machine learning automatically catches critical incidents. No more building complex alert rules or figuring out what to monitor.
Here are just a few examples. Try it with your app and see what we can catch!
SaaS Service, Fortune 100 company
Mission critical service experienced an authentication issue that blocked new users from logging in. It took six hours to detect and root cause the incident using existing APM and monitoring tools.
Zebrium ML auto-identified and found the root cause within seconds!
Organization behind two well-known CNCF OSS projects
Engineering team stress tested Zebrium Autonomous Log Monitoring using a popular chaos engineering tool to systematically create over a dozen application and Kubernetes failure scenarios.
Zebrium automatically detected and found root cause for every single incident.
Workforce automation SaaS platform
Soon after installation, Zebrium auto-detected a certificate issue caused by a subtle software bug. This prevented an outage of over 30 production services, and saved hours of lost productivity in troubleshooting.
Reduced MTTR
Lower your Mean Time To Resolve. We tell you the most likely root cause and take you straight to the specific events and context needed resolve the incident.
Built for Kubernetes
Ten things your log manager and monitoring can't do
Warning - if you try these with *, you will be disappointed!
*Insert your favorite log manager or monitoring tool here.
Find problems for you (without human guidance or definitions)
Automatically parse custom logs (even the gnarly ones)
Automatically extract embedded variables (metrics, strings, IP addresses, etc.)
Self-maintain the schema even when events change across software versions
Visualize event patterns (X-ray) of problems and what lead to them
Make search more useful by allowing you to easily see results by event type
With one click filter in our out any event type
Chart any metric or string directly from an event (without building manual parsing rules)
Suggest signatures to detect known conditions even those characterized by complex multi-event sequences
Extract errors and multiline stack traces (e.g. for python, java, go, etc.)
Do all of this without requiring a PhD (oops, that's number 11)
Too good to be true? Try it now.
Get started for free in less than 5 minutes.
Not ready to try it yet?
Pleas let us know if you have any questions or would like a live demo. We look forward to hearing from you.
