While Zebrium provides all the log management features you'd expect (aggregation, search, filtering, etc.), what sets it apart is its use of machine learning. Automatically see root cause without hunting, proactively detect new failure modes without building rules and enjoy fully structured logs without defining parsing expressions.
Use Zebrium as your primary log manager or to augment log mangers like the Elastic Stack.
Broad platform support
Kubernetes, Linux, Docker/ECS, syslog and CloudWatch. Most other platforms via Logstash (see integrations).
Automatic collection and storage in a scale-out MPP relational column store with cloud-scale scalability and query performance.
By logtype, severity, event type, labels (host, container, app, etc.) and any user defined expression. Views can be saved and reloaded.
Search and filtering supports full PCRE2 regex syntax.
Users are assigned roles and can be granted access to one or more deployment groups (e.g. test and production).
Machine learning is used to automatically structure log lines of any format without requiring manual parsing expressions.
Visualize and navigate user-defined sets of events. Stacking maps allows you to see event correlations.
One click charting
Since all event variables are automatically extracted, chart any string, numeric, IP address, etc. with just a click.
Search for answers
If you see a cryptic log event, automatically see Google or Stack Overflow search results.
Share your entire session, including filters, maps and current position for easy collaboration.
Proactive root cause reports
When looking for root cause, instead of hunting through logs, immediately see root cause by selecting the relevant proactive root cause report.
On-demand root cause scan
The "scan for root cause" button lets you perform on-demand scans for root cause around a specified time.
Core and related events
Root cause reports initially contain a core set of log events and correlated metric anomalies that describe what happened. If you require more detail, the ML can also pull in additional correlated errors and anomalies.
Root cause reports can be viewed on their own, or in the context of surrounding aggregated log lines.
When our ML detects a problem, you can choose to be alerted on future occurrences without building rules. Great for catching new/rare failure modes.
You can easily define a set of log events and conditions, and be alerted whenever they occur.
User-defined alerts often only detect symptoms. Zebrium can augments these alerts by using machine learning to uncover details of root cause.
Just install one of our lightweight log collectors or fork a copy of your logs using Logstash. Zebrium works with any app - no parsers, code changes, rules or config needed.