Statistical
Outlier Detection
Updated Jul 28, 2026 · 1d ago
Multi-dimensional outliers in log feature space. Catches subtle anomalies that single-axis thresholds miss.
Example alert
Exact wording varies — the detector generates titles from the anomaly it finds. This is representative of what an alert looks like when it fires.
How it works
Builds a multi-dimensional feature profile per service from structured log fields (status codes, latencies, sizes). Isolation forest scoring identifies requests that are rare across multiple dimensions simultaneously. Learning period: 7 days.
Availability
Runs on these tiers:
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Open alerts in the sandbox →Related detectors
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