With the Native Planning Engine now in preview and OneLake writeback coming soon, Fabric Planning can handle enterprise-scale models. Choosing the right approach upfront matters, and this post walks through how.

The short version:

• Most plans are far smaller than the semantic model beneath them. With filters and RLS, most organizations stay well under 1 million cells, which is comfortable for the majority of use cases.

• The bottleneck at scale is usually the fetch, not the engine. How much data each user pulls, and how many users pull it at the same time, is what to size first.

• When you do need to go bigger, there are four methods: Context Filtering, Dimension Partitioning, Multi-Dimensional Breakdown with Cube, and PowerTable Integration. Each is suited to a different shape of plan.

• Driver-based row models vs. measure models: If your logic already lives in DAX measures, extend it with a measure model rather than rebuilding it in the planning layer.

The post walks through all four scaling methods with guidance on when to use each, plus a comparison table for row vs. measure models.

Read it here.