Unexpected High Cost on Simple Use Case

Hi jandersoniqz 
The same thing has happened to me. We are currently testing and, while I understand that optimizations will improve over time, I think it is important to acknowledge something: AI workloads like this are not designed to run efficiently on small capacities such as F2 or F4.
In my opinion, Fabric IQ brings a lot of value and is something that you almost need to have in your roadmap. However, we also need to be aware that this value comes with a cost. This is where a key aspect comes into play: DataOps.
We are moving into more complex scenarios (real-time, agents, graphs, etc.), and this means it is no longer just about building solutions. We need to start thinking seriously about:
- The ROI of each use case
How to optimize capacity consumption
Which workloads truly justify this level of investment
As an additional recommendation, I believe it will be key to:
Properly separate environments (dev/test/prod) to avoid unnecessary capacity consumption
Monitor usage from the beginning
Prioritize use cases with real business impact
Set capacity limits, configure alerts, and ensure workloads do not block or saturate the capacity
Thanks for sharing your experience. If you notice any updates or improvements, it would be great if you could share them