AI Powered Observability

Accurate Detection without Alert Fatigue

Zebrium detects software incidents by finding hotspots of abnormally correlated anomalous patterns in logs and metrics (see How it Works). Setup takes less than five minutes and no manual training or rules are required.

Auto-detected incidents are presented as reports containing event sequences and charts that show root cause. Our multi-layered ML approach achieves high accuracy within a few hours and doesn't cause alert fatigue.

See  detailed examples of Zebrium auto-detected incidents. 

Use to Augment your Existing Tools or Use Standalone

Whether you already have an existing log manager or need a complete solution, Zebrium can help you catch critical incidents and determine root cause. 

Use with the Elastic Stack

The ZELK Stack option lets you use Zebrium in conjunction with Elastic Stack to catch software incidents and see root cause right inside Kibana. Integration is simple and doesn't require endpoint reconfiguration.

Augment your Existing Tools

Zebrium integrates with PagerDuty, Slack, OpsGenie and VictorOps to augment incidents detected by your existing monitoring, observability, APM, and other tools. Now you can use ML to find root cause for any incident plus catch rare incidents your other tools miss.

Use Standalone

Our log management and monitoring UI is optimized for Developers and DevOps with a focus on troubleshooting workflows. It provides easy incident drill-down, plus a full suite of aggregated log exploration features including regex search, filtering, one-click charting, alerting and more. 

Comparison with Other Observability Tools

The Evolution of Observability Tools

Logging, metrics and observability tools were built around aggregation, dashboarding, search and rule-based alerts. Some have evolved to include an anomaly detection (outlier detection) feature, mostly relating to metrics. This kind of anomaly detection can be  useful when drilling-down on a problem, but tends to suffer from too many false positives.

Zebrium takes a different approach. While Zebrium also detects anomalies in logs and metrics, it then uses a layered machine learning approach that find hotspots of abnormally correlated anomalous patterns. This produces accurate "Incident Reports" that not only detect that something is wrong, but also characterizes the root cause.