All you need to do is install install our log and metrics collectors - this takes less than 2 minutes. No parsers, libraries, code changes or config needed. After that, our Machine Learning (ML) takes over.
Within minutes, our ML learns the structures of your logs, and categorizes each event into a “dictionary” of unique event types. Categorization is crucial to being able to learn the patterns in your logs and metrics.
In the first hour, it learns the normal patterns of every event and metric (the learning keeps getting better as it sees more data).
When log or metric patterns change, our ML detects these as anomalies. But to separate signal from noise, it looks for hotspots of abnormally correlated anomalies across both metrics and logs.
The hotspots detected in step two are packaged into human readable incidents. Incidents make it easy for a user to clearly see the correlated set of anomalous log events and/or metrics.
Incident alerts are sent via Slack, email or webhook.
The entire process is completely autonomous - without the need for manual configuration, user-defined thresholds or alert rules.