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.
Whether you already have an existing log manager or need a complete solution, Zebrium can help you catch critical incidents and determine root cause.
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.
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.
We adhere to industry best practices for security including: encryption of data in flight, AES-256 encryption of data at rest, granular removal of sensitive records or fields, secure isolation of customer data and an option for a dedicated instance and VPC. All customer data will be deleted upon termination of service or by request. Read our security policy here.