Notice: New Anomaly Detector resources can no longer be created as of September 20, 2023, and the service is being retired on October 1, 2026. Microsoft recommends migrating to Microsoft Fabric (anomaly detection in Real-Time Intelligence) or to the open-source microsoft/anomaly-detector project.
What is Azure AI Anomaly Detector?
Azure AI Anomaly Detector is an AI service that automatically detects unusual patterns in time series data. The service uses machine learning to learn the normal behavior of your data and identify deviations. This happens without manual training or labeling of data.
The service offers two modes: Univariate Detection analyzes single metrics (e.g., CPU utilization), while Multivariate Detection recognizes correlations between multiple metrics (e.g., CPU, memory, and network together). Multivariate analysis finds anomalies that would go undetected when examining individual metrics in isolation.
Core Features
- Univariate and multivariate time series analysis
- Unsupervised learning without manual training
- Real-time detection for streaming data
- Batch analysis for historical data volumes
- Explainable results with confidence scores
Typical Use Cases
Manufacturing companies use Anomaly Detector for predictive maintenance: Sensor data from machines is continuously analyzed. Deviations in vibration patterns or temperature trends are detected before a defect occurs.
Financial institutions deploy the service for fraud detection. Unusual transaction patterns that deviate from historical customer behavior are detected in real-time and flagged for manual review.
IT teams monitor infrastructure metrics. The service detects anomalies in server performance, network traffic, or application latency and automatically triggers alerts.
Benefits
- No ML expertise required
- Automatic adaptation to seasonal patterns
- Low latency for real-time applications
- Integration into existing monitoring pipelines
Integration with innFactory
As a Microsoft Solutions Partner, innFactory supports you with Azure AI Anomaly Detector: use case analysis, data connection, alert integration, and operational optimization.
Typical Use Cases
Frequently Asked Questions
What data types does Anomaly Detector support?
The service specializes in univariate and multivariate time series data. Typical examples include sensor data, transaction volumes, server metrics, or business KPIs with regular time intervals.
Do I need to train the model?
No, Anomaly Detector is an unsupervised ML service. It automatically learns from historical data and detects deviations from normal patterns without manual labeling.
How quickly are anomalies detected?
For real-time detection, analysis takes a few milliseconds per data point. Batch analyses for historical data scale linearly with data volume.
Can I integrate Anomaly Detector with Azure Monitor?
Yes, via Azure Functions or Logic Apps, Anomaly Detector results can trigger alerts in Azure Monitor. Native integration for automated monitoring.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Azure (official documentation). This page does not represent an offer by Azure.
