Configure anomaly detection monitors in Datadog to alert on unusual metric patterns

Steps

1. Create a monitor via POST /api/v1/monitor with type set to metric alert and a query using the anomalies() function: anomalies(metric_name{scope}, 'algorithm', deviation_count)
2. Choose the algorithm parameter: basic for metrics without seasonality, agile for metrics that shift level over time, or robust for metrics with frequent spikes that should not reset the baseline
3. Set the deviation_count threshold (e.g., 2) to control sensitivity; lower values alert more aggressively and higher values require a larger deviation before firing
4. Configure the thresholds object with critical and warning values (expressed as fraction of time anomalous, e.g., 1.0 means the entire evaluation window must be anomalous for critical)
5. Set options.seasonality to hourly, daily, or weekly so the model accounts for time-of-day or day-of-week patterns in the metric
6. Validate the monitor by viewing the Preview graph in the Datadog UI to inspect historical anomaly detection overlays before enabling notifications