Mar 17, 2025

Maximizing Performance, Minimizing Costs: Power BI Embedded & Fabric Optimization Strategies

ByPascal Kiefer

Maximizing Performance, Minimizing Costs: Power BI Embedded & Fabric Optimization Strategies

Power BI Embedded and Fabric costs are capacity-based, meaning pricing depends on the selected SK and the allocated compute resources.

Power BI Embedded: Costs are based on the chosen SKU (A1–A6) and are billed per hour, regardless of usage. Higher SKUs provide more memory and processing power but cost more.

Microsoft Fabric: Pricing is based on Capacity Units (CU), with different SKUs (F2–F2048). Usage includes query processing, refresh workloads, and AI-powered features, and costs increase with higher demand and scaling.

But as both Power BI Embedded and Fabric use capacity-based pricing, performance inefficiencies can drive up costs significantly. Optimizing performance ensures that workloads run smoothly without overloading expensive capacity tiers. Optimizing performance not only enhances user experience but also helps reduce the consumption of computing resources, leading to substantial cost savings. In this blog, we will explore key strategies to optimize Power BI Embedded and Fabric capacities while keeping costs under control.

Why Performance Optimization Matters for Cost Reduction

Power BI Embedded and Fabric capacities are billed based on the SKU and computing power consumed. Poorly optimized reports, inefficient data models, and excessive query execution can lead to unnecessary overuse of resources. Key benefits of performance optimization include:

Lower Capacity Consumption: Well-optimized reports and models use fewer compute resources.

Faster Load Times: Performance tuning reduces wait times, improving the user experience.

Scalability: Efficient resource management enables scaling without the need for costly upgrades.

Cost Efficiency: Avoiding inefficient queries and large datasets prevents exceeding capacity limits.

Key Performance Optimization Strategies

Below are the most effective strategies for optimizing your Power BI Embedded and Microsoft Fabric implementations:

Optimize Data Models for Efficiency

A well-structured data model is crucial for performance and cost optimization. Well designed models increase processing time and resource usage.

Best Practices:

Use Star Schema Instead of Flat Tables: Star schemas improve query performance by reducing the number of joins.

Reduce Data Granularity: Instead of storing highly detailed records, aggregate data where possible.

Avoid Large Tables in DirectQuery: DirectQuery fetches data on demand, and large tables can slow performance significantly.

Impact on Costs: Optimized data models require fewer resources, reducing the overall compute usage in Power BI Embedded and Fabric.

Optimize DAX Queries and Measures

DAX (Data Analysis Expressions) is used for calculations in Power BI. Poorly optimized DAX queries can slow down reports and increase capacity consumption.

Best Practices:

Use Variables: Variables prevent repeated calculations and improve efficiency.

Reduce Row Context in Measures: Avoid iterators like SUMX when possible; use pre-aggregated columns instead.

Minimize the Use of FILTER and CALCULATE: These functions can be expensive when working with large datasets.

Impact on Costs: Faster DAX queries lead to lower CPU usage, reducing the need for higher-capacity tiers.

Optimize Report Visuals and Layout

The number and type of visuals in a Power BI report directly impact performance. A cluttered report with too many visuals increases rendering time and resource consumption.

Best Practices:

Limit the Number of Visuals: Too many visuals on a single page increase query load.

Use Aggregations Instead of Detailed Tables: Displaying summary data instead of row-level data reduces processing.

Reduce the Use of High-Complexity Custom Visuals: Some custom visuals can be resource-intensive.

Impact on Costs: A streamlined report reduces processing power, allowing more efficient use of capacity and potentially allows changing to a lower SKU.

Implement Row-Level Security (RLS) Wisely

RLS is a powerful feature that controls access to data based on user roles. However, if not optimized, it can lead to high processing costs.

Best Practices:

Avoid Complex RLS Filters: Complex security rules create additional processing overhead.

Use Static RLS Where Possible: Pre-filtered datasets are more efficient than dynamic filtering.

Test RLS Performance with Large User Groups: Ensure that security filters do not slow down report execution.

Impact on Costs: Efficient RLS reduces query processing overhead, leading to better capacity utilization.

Use Incremental Refresh for Large Datasets

Importing large datasets daily or refreshing entire datasets frequently increases capacity usage. Incremental refresh allows Power BI to update only new or changed data, significantly reducing processing load.

Best Practices:

Enable Incremental Refresh: Configure refresh policies for large datasets.

Partition Data Efficiently: Break data into manageable chunks to reduce processing time.

Limit Historical Data Retention: Only keep the necessary amount of historical data in the model.

Impact on Costs: Incremental refresh minimizes memory usage and avoids unnecessary data processing, reducing compute costs.

Optimize Dataflows and Storage Mode

Using the right storage mode and optimizing dataflows can improve performance while reducing costs.

Best Practices:

Use Hybrid Tables: Combine Import and DirectQuery for better performance.

Cache Frequently Used Data: Reduce the load on external data sources by caching key data.

Optimize Dataflows with Computed Entities: Precompute complex transformations instead of performing them at runtime.

Impact on Costs: Efficient storage and dataflows reduce the need for high compute power, optimizing capacity usage.

Monitor Performance and Adjust Capacity Allocation

Regularly monitoring Power BI Embedded and Fabric capacity usage helps in identifying performance bottlenecks and optimizing resource allocation.

Best Practices:

Use the Power BI Capacity Metrics App: Track CPU, memory, and query performance in real-time.

Identify High-Consumption Reports: Optimize reports that consume excessive resources.

Auto-Pause and Scale Capacity: Use Fabric's autoscaling features to dynamically allocate resources based on demand.

Impact on Costs: Proactive monitoring helps prevent overuse of high-cost capacity tiers, ensuring cost efficiency.

Conclusion

Optimizing performance in Power BI Embedded and Fabric capacities is essential for maintaining efficiency while controlling costs. By refining data models, optimizing queries, streamlining reports, and managing refresh strategies, businesses can significantly reduce their Power BI expenses without compromising functionality.

With a structured approach to performance tuning, organizations can maximize their analytics capabilities while minimizing costs, ensuring a scalable and cost-effective Power BI implementation.

Need help optimizing your Power BI Embedded or Microsoft Fabric implementation? Contact us to discuss how Embedsy can help you achieve better performance while reducing costs.