Fabric RTI 101: AI Anomaly Detector - Limitations

As with any preview feature, there are some practical considerations.

The first is that this is still evolving. Not every scenario or data type is supported yet. Expect some limits on dataset size, refresh frequency, and concurrent analyses.

You must enable the Python plugin in the Eventhouse. This is what provides the model runtime, and enabling it isn’t instant. If you attempt analysis before it’s ready, the system will return an error.

Sufficient historical data is essential. The AI model needs context to learn what normal looks like. If you only have a day or two of data, it may not capture normal seasonality or trends. A good rule of thumb is to have at least one full pattern cycle - for example, a few weeks of hourly data or several months of daily data.

Currently, each detector supports one active model configuration. If you want to monitor multiple metrics, you’ll create multiple detectors.

Performance-wise, keep in mind that the Eventhouse allows a limited number of concurrent queries. Running several detectors or analyses at once could slow performance or trigger timeouts.

Microsoft had a standalone Azure Anomaly Detector service. They have announced that it will retire in October 2026. So this Fabric-integrated capability is the path forward.

If you’re currently using Azure’s older service, start planning a migration so you can take advantage of the unified monitoring and alerting capabilities in Fabric.

Overall, it’s a promising feature that already covers most typical scenarios, but it’s best treated as an evolving component rather than a finished product.

Learn more about Fabric RTI

If you really want to learn about RTI right now, we have an online on-demand course that you can enrol in, right now. You’ll find it at Mastering Microsoft Fabric Real-Time Intelligence

2026-10-02