Let machine learning catch software problems other tools can't

Existing monitoring and log management only finds issues/symptoms you define. We also find the critical "unknowns" that can wreak havoc.

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Free log file anomaly detector
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Relax - we'll tell you as soon as something's wrong!

We use unassisted machine learning to detect critical software problems. We show you a breakdown of issues and guide you to resolution. Our ML-based anomaly detection alerts you of new critical issues as soon as they happen. And we make it dead-simple to be alerted on issues you know about. 

While log management and monitoring tools are good at finding pre-defined symptoms, Zebrium auto-detects anomalies, known issues, exceptions and errors without you having to do anything.

Examples of issues our anomaly detection found:

Detected critical authentication issue six hours before the APM and monitoring tools caught the problem. Detected a database restart in a stack trace which would have led to service disruptions. Prevented an outage by catching a crash of a critical service. Quick drill down exposed memory buffer overflow as the root cause. Detected a rare error while saving encryption keys, which would have led to loss of data if undetected. Auto-detected a spike in cache refresh time that impacted user experience (missed by monitoring tool as metric had not been instrumented).

How are we different?

We use machine learning to perfectly structure logs and metrics. Each log line is schematized by event type, with parameters captured into their own typed columns and optimized for query. This results in a foundational “dictionary” of unique event types that is used to accurately learn normal patterns and reliably detect “anomalies” - when events break pattern. We do not require software changes, scripting or pre-training and everything is self-managed across software versions

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