Leading Semantic Layers Compared > Power BI Comparison Report
Kyvos Semantic Layer vs. Power BI Semantic Models
Make an informed decision by choosing the true semantic layer
Download the full report if:
- Your query responses take more than a second
- Analytics performance degrades with increasing data volumes
- Analytics costs are going through the roof
- You need seamless connectivity with all data platforms
- User concurrency is limited by memory
- You need faster, more comprehensive data modeling
- Data refresh takes too long, especially if semantic models are large or complex
- You need advanced Gen AI capabilities for true self-serve analytics
Executive Summary
Power BI, with or without Microsoft’s Direct Lake Architecture, is proposed by some people as an enterprise semantic layer solution. It is not that and shouldn’t be implemented as one. Power BI semantic models have limited functionalities, especially when dealing with large, complex datasets and thousands of concurrent users.
Kyvos is a universal semantic layer that checks all the right boxes. It simplifies analytics, improves query performance and establishes data trust—without any constraints of speed, scale or cost.
A Quick Comparison
| Feature | Kyvos Semantic Layer | Power BI Semantic Model |
|---|---|---|
| CONNECTIVITY WITH DATA PLATFORMS (PBI SEMANTIC MODEL ON MS FABRIC) | ||
| Data Platforms Supported | No platform lock-in | Only OneLake |
| BI TOOL CONNECTIVITY | ||
| Excel Add-In | Yes | No |
| Tableau Named Connector | Yes | No |
| MicroStrategy Named Connector | Yes | No |
| LangChain Connectivity | Yes | No |
| QUERY PERFORMANCE | ||
| User Concurrency | 1000+ users | Limited by memory |
| DATA SCALE | ||
| Model Size | Unlimited | 100 GB max |