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The error message certainly points toward a quota-related restriction, but one detail stands out: you are seeing 0 available vCPUs across multiple VM families and multiple regions. That is not the most common pattern for a simple exhausted quota.
In addition to reviewing the regional and VM-family quotas already mentioned, I would verify whether the subscription has actually been granted compute quota in those regions.
A useful validation is to check Subscriptions -> Usage + quotas -> Compute and compare:
- Total Regional vCPUs
- The specific VM-family quota (Dv5, DCv5, etc.)
- Current usage versus limit
If the limit itself is 0 (not just available = 0), that typically indicates the subscription does not currently have quota allocated for that region or VM family rather than quota being consumed by existing resources.
Since you tested both Standard_D4ds_v5 and Standard_DC4as_v5 and saw similar behaviour across East US and Germany West Central, I would also check whether the same subscription can deploy a small standalone Azure VM in those regions. If a basic VM deployment fails with a similar quota restriction, that helps confirm the problem exists at the Azure Compute subscription layer rather than within Azure Databricks itself.
Another point worth checking is whether there are any subscription-level spending limits, offer-type restrictions, or recently created subscription constraints that may result in very low or zero initial compute quotas. A PAYG subscription does not automatically guarantee pre-allocated vCPU capacity in every region and VM family.
Based on the symptoms described, this appears more likely to be a subscription-level compute quota allocation issue than a Databricks cluster policy, cluster configuration, or Spark related problem.
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