Everything you need for
anomaly detection
A modern, self-supervised engine that drops in where Azure left off.
Azure API Compatibility
Canary Edge accepts the same JSON format and exposes the same endpoints as Azure Anomaly Detector. Your existing client code works without modification -- change the base URL and you are done.
/detect/entire-- batch detection over the full series/detect/last-- streaming detection on the latest point- Identical request and response JSON schema -- zero code changes
Field Mapping
| Azure Field | Canary Field | |
|---|---|---|
series | series | |
series[].timestamp | series[].timestamp | |
series[].value | series[].value | |
granularity | granularity | |
maxAnomalyRatio | maxAnomalyRatio | |
sensitivity | sensitivity | |
customInterval | customInterval | |
period | period | |
isAnomaly | isAnomaly | |
isPositiveAnomaly | isPositiveAnomaly | |
isNegativeAnomaly | isNegativeAnomaly | |
expectedValues | expectedValues | |
upperMargins | upperMargins | |
lowerMargins | lowerMargins |
Encoder
Compress raw time-series into latent representations
Predictor
Forecast expected latent state from context window
Energy Scorer
Compute reconstruction energy as anomaly signal
Regime Classifier
Map energy to operational regime via z-score thresholds
LeWM World Model
Canary Edge is powered by LeWM -- a Learned World Model inspired by Joint-Embedding Predictive Architecture (JEPA). It learns the normal dynamics of your system without any labeled anomaly data.
Self-supervised learning
No labels required. The model learns normal behavior by predicting its own latent representations.
Reconstruction energy
Anomalies produce high energy scores when the predictor fails to match the encoder output.
Continuous adaptation
Fine-tune the predictor head on each machine to capture unique operational patterns.
Real-time Detection
Sub-50ms inference at the 99th percentile. Canary Edge is optimized for production workloads that demand low-latency anomaly detection.
Batch mode
Submit an entire time series and receive anomaly flags for every point. Ideal for historical analysis and backfilling.
Streaming mode
Send the latest data point and get an instant determination. Designed for live monitoring and alerting pipelines.
Production-grade SLA
Engineered for 99.9% uptime with horizontal scaling and graceful degradation under load.
Inference Latency (p99)
Canary Edge
48ms
Azure AD
220ms
AWS Lookout
350ms
Open Source (PyOD)
480ms
Throughput
12,000
requests/sec (batch)
45,000
requests/sec (streaming)
Sensor Model
High-frequencyVibration, temperature, pressure sensors sampled at sub-second intervals. Captures fast transient anomalies.
Metrics Model
Low-frequencyBusiness metrics, daily aggregates, hourly counters. Optimized for trend shifts and seasonal deviation.
Multi-resolution Models
One size does not fit all time series. Canary Edge ships two model variants optimized for different data characteristics and automatically selects the right one based on your series.
Automatic selection
The API inspects granularity and sample rate to route your data to the optimal model. No configuration needed.
Tuned architectures
Each variant has a purpose-built encoder depth, context window, and feature extraction pipeline.
Unified API
Both models expose the same request/response contract. Swap between them transparently.
Per-machine Fine-tuning
Start with a generic pre-trained model, then fine-tune the predictor head on each individual machine. The system learns what "normal" looks like for your specific equipment.
Zero-shot baseline
The generic model provides accurate detection out of the box. No training period required to start.
Automatic adaptation
As data flows in, the predictor head adapts to each machine's unique patterns and operating modes.
Continual improvement
Detection accuracy improves over time as the fine-tuned model captures more operational context.
Fine-tuning Progression
Generic Model
82%Pre-trained encoder and predictor provide a strong baseline for all machine types.
Initial Fine-tune
91%After 24 hours of data, the predictor head begins adapting to machine-specific patterns.
Mature Model
97%After 7 days, the model captures seasonal patterns, maintenance cycles, and load variations.
HEALTHY
z < 1.0Normal operating conditions. Energy scores within one standard deviation of the running baseline.
ACTIVE
1.0 <= z < 2.0Elevated activity. Mild deviations that may indicate changing load, warm-up, or non-critical drift.
TRANSITION
2.0 <= z < 3.0Warning zone. Significant deviation from normal behavior. Investigate or prepare for intervention.
SHOCK
z >= 3.0Critical anomaly. Extreme energy spike beyond three standard deviations. Immediate attention required.
Regime Classification
Beyond binary anomaly flags, Canary Edge classifies every data point into one of four operational regimes. This gives operators context-aware severity levels instead of just "anomaly or not."
Z-score thresholds
Regimes are determined by the standardized distance of the energy score from the running baseline mean.
Actionable severity
Each regime maps to a clear operational response: monitor, investigate, or act immediately.
Temporal tracking
Track regime transitions over time to identify degradation trends before they become critical failures.
How Canary Edge Compares
A side-by-side comparison of anomaly detection platforms.
| Feature | Canary Edge | Azure AD | AWS Lookout | Open Source |
|---|---|---|---|---|
| Azure API Compatible | ||||
| Self-supervised (no labels) | ||||
| P99 Latency | <50ms | ~220ms | ~350ms | varies |
| Per-machine Fine-tuning | manual | |||
| Regime Detection | ||||
| Streaming + Batch | ||||
| Multi-resolution Models | ||||
| Starting Price | $0 free tier | Retired | $0.05/1K inf. | Self-host |
Ready to migrate?
Switch from Azure Anomaly Detector in under 90 seconds.