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Azure Anomaly Detector Migration Guide

Azure Anomaly Detector retires October 1, 2026. WaveGuard is a drop-in replacement.

Key Differences

Feature Azure Anomaly Detector WaveGuard
API calls 3+ (create model, train, detect, delete) 1
State Stateful (model persists) Stateless (nothing stored)
Training time Minutes to hours Seconds (included in scan call)
Data types Time series only Any (JSON, text, numbers, time series)
Model management Required None
GPU Azure-managed NVIDIA T4 (Modal serverless)
Pricing Per-transaction + storage Per-scan (no storage costs)

Migration Steps

1. Install the SDK

pip install WaveGuardClient

2. Replace the client

Before (Azure):

from azure.ai.anomalydetector import AnomalyDetectorClient
from azure.core.credentials import AzureKeyCredential

client = AnomalyDetectorClient(endpoint, AzureKeyCredential(key))

# Create and train model (separate step, takes minutes)
model = client.train_multivariate_model(training_request)
# ... wait for training ...

# Detect (separate step)
result = client.detect_multivariate_batch_anomaly(model_id, detect_request)

# Clean up (separate step)
client.delete_multivariate_model(model_id)

After (WaveGuard):

from waveguard import WaveGuard

wg = WaveGuard(api_key="YOUR_KEY")

# Everything in one call
result = wg.scan(training=normal_data, test=new_data)

3. Map your data format

Azure requires time-series CSV with timestamps. WaveGuard accepts anything:

# Azure format (time series only):
# timestamp,cpu,memory,errors
# 2025-01-01T00:00:00Z,45,62,0
# 2025-01-01T01:00:00Z,48,63,0

# WaveGuard format (any of these work):
training = [
    {"cpu": 45, "memory": 62, "errors": 0},  # JSON objects
    {"cpu": 48, "memory": 63, "errors": 0},
]

# Or time series arrays:
training = [
    [45, 48, 42, 50, 47],  # window 1
    [46, 49, 43, 48, 45],  # window 2
]

# Or even text:
training = [
    "INFO Request processed in 45ms [200 OK]",
    "INFO Request processed in 52ms [200 OK]",
]

4. Map response fields

Azure Field WaveGuard Field
is_anomaly result.is_anomaly
severity result.score
score result.confidence
interpretation result.top_features

5. Remove Azure cleanup code

Azure requires explicit model deletion. WaveGuard is stateless — there's nothing to clean up.

# DELETE THIS:
# client.delete_multivariate_model(model_id)

# WaveGuard tears down automatically after each scan() call

FAQ

Q: Do I need to retrain periodically? A: No. WaveGuard is stateless — you send training data with every call. Update your normal baseline whenever your definition of "normal" changes.

Q: What about my existing Azure training data? A: Convert your CSV rows to JSON objects or arrays, and pass them as the training parameter. No model files to migrate.

Q: Is the detection quality comparable? A: WaveGuard uses a fundamentally different approach (wave physics vs. ML). Results may differ. Test with your data before fully migrating.

Q: What about latency? A: First call may take 5-10s (GPU cold start). Subsequent calls: ~1-3s depending on sample count. Azure typically takes longer due to separate train/detect steps.

Example

See examples/05_azure_migration.py for a complete working example.