Fabric Semantic Model Vs SSAS Tabular Cube

Thankyou, nielsvdc, for your response.

Hi Anusha66,

Based on my understanding, slow rendering typically occurs when Fabric semantic models default to Direct Lake and queries fall back to DirectQuery, for example when unsupported features are used or when the data is not cached in memory. In such cases, visuals tend to be slower compared with the in-memory behaviour of SSAS Tabular.

Please follow the steps below, which may help improve performance:

1. Check for Direct Lake fallback by using Performance Analyzer. If visuals show DirectQuery time, fallback is occurring.
2. For the best performance, switch large tables to Import mode if data freshness permits, or adopt a hybrid approach. If you continue to use Direct Lake, ensure the Delta tables are optimised with V-Order or compaction.
3. Design the model following best practices: maintain a star schema, remove high-cardinality columns, and apply aggregations.
4. Monitor capacity using the Fabric Capacity Metrics app to verify whether the F64 is experiencing CPU or memory pressure.

Additionally, please refer to the links below for further information:
Semantic model modes in the Power BI service - Power BI | Microsoft Learn
Direct Lake overview - Microsoft Fabric | Microsoft Learn
Use Performance Analyzer to Examine Report Performance - Power BI | Microsoft Learn
Understand the metrics app compute page - Microsoft Fabric | Microsoft Learn
Optimization guide for Power BI - Power BI | Microsoft Learn

We hope the information provided helps resolve the issue. Should you have any further queries, please feel free to contact the Microsoft Fabric community.

Thank you.