Does Microsoft Fabric Automatically Autoscale SKUs Based on Compute Demand?

Hi GhulamMustafa 

No, Fabric doens;t scale automatically to your capapcity from F2 to higher SKU's like F4. For example if your workload crosses the compute limits of your F2 SKU, you may face throttling issues. 

Yes, you can do automatic scaling by using Azure Logic apps read this blog Microsoft Fabric: Automating Fabric Capacity Scaling with Azure Logic Apps - BI Insight it might helps you.

and if  you want, you can  do manual upgrading also to higher SKU's. read more about scaling here Scale your Fabric capacity - Microsoft Fabric | Microsoft Learn

Please let me know if it helps you.

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Hi GhulamMustafa ,

The answer from suparnababu8  is correct - Fabric doesn't automatically scale between SKUs. But let me clarify a few important points about Fabric's scaling behavior:

What happens within your SKU: Fabric does provide some automatic scaling within your purchased capacity. For example, if you have F4, it can dynamically allocate compute units across different workloads (notebooks, pipelines, etc.) based on demand.

What doesn't happen:

• No automatic upgrade from F2 to F4 when you hit limits
• When you exceed your SKU's capacity, you get throttling or queuing
• You'll see slower performance rather than automatic scaling up

Your scaling options:

Manual scaling: Upgrade/downgrade through the Fabric portal anytime. Takes a few minutes and you only pay for what you use.

Automated scaling via Logic Apps: As @Sree1994 mentioned, you can build automation using Azure Logic Apps to monitor capacity metrics and scale up/down based on usage patterns.

Bursting behavior: Some Fabric SKUs allow short-term bursting above baseline capacity, but this isn't the same as scaling to a higher SKU tier.

Best practice: Monitor your capacity utilization in the Fabric portal. If you consistently hit limits, consider moving to the next SKU tier rather than dealing with throttling.

Cost consideration: Since Fabric charges per second of compute, it's often better to scale up temporarily for heavy workloads rather than staying throttled on a smaller SKU.

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