Forum Discussion
AI functionality using Power BI Dataset
Hello everyone,
I am trying to introduce AI funcationality to our users, which will be based on the datasets in PBI service.
I got a F2 capacity and enabled copilot which worked a little and then the stopped since the compute units were consumed.
Once the units were consumed, the reports in that workspace would not load since the compute units were consumed.
I talked to the PBI support team and the Azure team as well and they said that the capacity is thorttling and that it will take time to normalize again and if I turn the capacity on and off, it will work but I will have to pay for it etc. etc.
I understand that the F2 capacity is small but I do not mind if my prompts take longer time to return an answer, cause even if I increase the capacity size, eventually it is going to hit that threshold of consuming compute units.
Is there any way that I can enable use of AI capabilities based on the datasets in PBI Service, in a safe and secure way with RLS enabled, without hitting any thresholds and without providing user access to the desktop files?
Again, I do not mind if the prompts take a little longer for returning results, due to a small capacity.
Thank you,
Vishesh Jain
Hi visheshjain
Your concern is valid. Copilot workloads can spike CU consumption quickly, especially on smaller SKUs like F2. While CU smoothing averages usage over 24 hours, throttling can still occur during short-term bursts if the instantaneous consumption exceeds the allocated capacity.
12 Replies
Hi. F2 is really small to use both the interactivity of reports and backgroup data operations + copilot. F2 will help you to understand how it's used, but it won't work with a productive environment using both. As an alternative you could purchase another F2. Keeping two F2. You can change at admin portal settings that you want one F2 to be exclusively for Copilot. That way it will never use the reports capacity. It will take time to answer the AI but it won't make trouble with reports. The setting is:
You can find more info here: https://learn.microsoft.com/en-us/fabric/enterprise/fabric-copilot-capacity
I hope that helps,
1. Set up Surge Protection for that capacity. Setting it somewhere to 40% will leave enough room for interactive queries while blocking background (which includes CoPilot)
2 Get a (much) bigger capacity. F2 is too small for CoPilot.
Hi visheshjain
Following up to confirm if the earlier responses addressed your query. If not, please share your questions and we’ll assist further.Hi,
Yes I went through the solutions provided and none of them really work.
I turned on surge protection but in that case copilot usage is very limited and if I use the entire capacity as a Copilot capacity then the report refreshes stop completeley.
I spoke to Microsoft over multiple tickets and they said that I have depleted my 'compute units' which even they cannot tell that how long will it take to normalize. I did deploy the Fabric Metrics Capacity app but still no answer to this questions.
Upgrading it to a F4 capacity will increase the number of 'compute units' but it will also have the same limitations with higher thresholds, not to mention the cost associated with the F4 capacity will be double.
Is there some way to use an MCP server with Power BI service since users will not have access to the desktop files.
Or we can create a custom visual, which will be connected to a LLM in the backend...I'm just spitballing here.
Thank you,
Vishesh Jain
In my experience you need at least a F128 for a meaningful Copilot experience. Even a F64 can be brought to its knees by a handful of concurrent Copilot users. Ask me how I know.
CU smoothing happens over a 24 hour period, mostly.
Your question is exactly the one many teams hit after trying native Copilot: how do you give users AI on top of a Power BI dataset without burning expensive capacity or breaking the experience when compute runs low? One pattern that worked well for us was using a custom in-report assistant instead of relying on the generic Copilot path. We built PowerMind for that, customized to the semantic model, designed for dashboard use, and much easier to roll out when you need model-aware behavior.
https://www.linkedin.com/posts/querixa_powerbi-microsoftfabric-dataanalytics-activity-7439451185696305152-wfFC?utm_source=share&utm_medium=member_desktop&rcm=ACoAACyDUKQBQhomDtRhx1YhG-beulUuVD5DR8wThat is parfect program
