Data keeps piling up, but teams still need answers fast and clean. Scaling the right tools makes all the difference when you’re building dashboards or planning capacity. That’s where Microsoft Fabric and Power BI come into play.
Bringing these platforms together helps businesses handle bigger models, real-time workloads, and deeper insights without things slowing down. From Dynamics data to live queries, upscaling isn’t just smart, it’s necessary. However, scaling is more than just about size. It’s about knowing what each piece brings to the table and how they work together.
Fabric Capacity Scaling and Features
Fabric doesn’t ask you to guess. Instead, it gives you clear capacity options. From F2 to F2048, you pick what matches your business needs. It’s built to scale up and down, whether you’re testing reports or building out enterprise-level models.
Capacity also affects what you can run in parallel, how fast you process data, and what storage limits apply. So, before you commit, know what matters most, which is speed, size, or concurrency. The Power BI vs. fabric discussion often comes down to what features you need on each end.
Planning Your Fabric Capacity
Not every team needs to jump into the biggest SKU. Most start small and then test. They will evaluate and then adjust accordingly. An important thing to learn here is that what matters is balance. If your model keeps timing out, scale up. If your load is light, scale down. In addition to that, you need to keep an eye on:
· Refresh speeds
· concurrent queries
· Peak-hour usage
Microsoft offers tools to help monitor each point. Proper planning here avoids bottlenecks and unnecessary costs. Fabric vs. Power BI isn’t always a pick-one situation. It’s about finding how both play their part.
Additional Resources to Help You Out
Microsoft provides a ton of guides on how to use Fabric with Dynamics, Power BI, and OneLake. If you’re unsure where to begin, check out Microsoft Learn, Fabric documentation, or the Power BI blog for updates.
Also, test environments are your friend. Don’t roll out anything new without checking limits, costs, and outcomes first.
Power BI Testing and Findings
Real insights come from real testing. When you push Power BI through Fabric capacity, the numbers tell the story. We ran several scenarios, scaling up and down, switching between models, and testing features side-by-side. The results showed what Microsoft Fabric vs Power BI looks like in action, not theory. Here’s what we saw.
Import Model
We kicked things off with a standard import model. Nothing fancy, it was just tables from Dynamics, some calculated columns, and light transformations. The performance was much smoother on F6. There were no timeouts with queries returning fastest and CPU usage staying steady.
Model Information
The import model had around 250 million rows across multiple fact and dimension tables. Compression helped, but even then, refresh time was around 10 minutes on F8, and half that on F16.
F16 SKU
This capacity handled high loads easily. Visuals updated fast. Even during heavy usage, performance didn’t dip. If you’re wondering how Power BI vs. fabric functions at this level. Fabric supports large models, and Power BI benefits from the compute behind it.
F8 Scale Down
Scaling down to F8 was fine for lighter use, but not for teams with many concurrent users. Refresh speeds slowed, and DAX queries lagged. If reports are used often, F8 may be too tight.
F16 Scale Up
Back on F16, we added more users and ran background jobs. The best thing about it was that there were no lags. Refreshes and visuals stayed responsive. It is safe to say that F16 is a safe midpoint for organizations growing into their analytics.
Direct Lake Model
Direct Lake was the real surprise. Instead of importing data, it reads straight from OneLake. That skips the dataset refresh wait and keeps everything live. With Fabric, this option becomes far more usable.
Model Information
This setup ran on a dataset of 100M rows. Performance was quick, with no refresh window. Data was always current, and usage didn’t spike Fabric capacity too much, unless you ran dozens of concurrent reports.
Summary
Upscaling doesn’t have to be complex. Start small, test smart, and grow with what fits. Whether you’re using Microsoft Fabric vs Power BI, or combining both, success lies in knowing what each platform handles well. Plan your capacity and measure your results to adjust as needed.
If you need help making the most of your fabric and Power BI setup, then CubePeaks is here to help you out. Our experts help organizations scale their analytics across Fabric, Power BI, and Dynamics.
Whether you’re exploring options or hitting resource limits, we’ll guide you through sizing, testing, and performance optimization. Get the insights you need on your terms.