victoriametrics/vmanomaly repository overview

⁠VictoriaMetrics vmanomaly

⁠About

In a fast-paced and complex landscape of system monitoring, VictoriaMetrics Anomaly Detection⁠ (vmanomaly), a part of our Enterprise offering⁠, serves as an observability layer for SREs and DevOps teams atop of collected data to automate the detection of anomalies in time-series data, reducing manual efforts required to identify abnormal system behavior.

Unlike traditional threshold-based alerting, which relies on raw metric values and requires constant tuning and maintenance of thresholds and alerting rules, vmanomaly introduces a unified, interpretable anomaly score⁠ - a de-trended, de-seasonalized metric generated through machine learning. This approach eliminates the need for frequent manual adjustments by enabling stable, long-term static thresholds (as simple as anomaly_score > 1) that remain effective over time through continuous model retraining.

By shifting to anomaly-based detection, teams can identify and respond to potential issues faster, enhancing system reliability and operational efficiency while significantly reducing the engineering effort spent on handcrafting and maintaining alerting rules.

⁠Documentation

Please head to official docs⁠

⁠Practical Guides and Installation

Begin your VictoriaMetrics Anomaly Detection journey with ease using our guides and installation instructions:

• Quickstart: Check out how to get vmanomaly up and running here⁠.
• Overview: Find out how vmanomaly service operates here⁠.
• Integration: Integrate anomaly detection into your observability ecosystem. Get started here⁠.
• UI: Finetune the configurations⁠ before deploying them on prod.
• Installation Options: Select the method⁠ that aligns with your technical requirements (docker, helm charts, vm operator).

⁠Licensing

Starting from 1.5.0⁠, vmanomaly requires a license key to run⁠. You can obtain a trial license key here⁠.