How to force Azure AI model deployment only point to single AI resource

Question

How to force Azure AI model deployment only point to single AI resource

Pulin Gupta 0

Deploying model in model catalog under a foundry is not using the correct parent foundry but is pointing to another foundry and also displaying another foundry for some reason. And since the foundry selected does not belong to the project, it throws error for quota.

Sridhar M 5,335 Reputation points Microsoft External Staff Moderator

2026-02-05T13:04:30.19+00:00
Hi Pulin Gupta

You’re experiencing deployment issues because the model deployment is being associated with an incorrect parent foundry, which does not belong to your project. As a result, the deployment fails during quota validation since quotas are enforced at the foundry level. This is a configuration and context issue rather than a model or quota misconfiguration.

Verify Resource Configuration

Start by signing in to the Azure AI Foundry portal and navigating to your project. Open the associated Azure OpenAI resource and verify that it is correctly linked to the intended foundry. Confirm the subscription, resource group, and workspace alignment to ensure the project and foundry are correctly associated.

Go to the Azure AI Foundry portal.

Access your Azure OpenAI resource and check the configuration settings. Ensure that everything is set according to the deployment requirements.

Ensure the Correct Foundry Is Selected:

During deployment, carefully confirm that the parent foundry selected belongs to the current project. If a different foundry appears, this may be due to cached session data or previously used contexts.

Re-select the correct foundry, and ensure it exists in the same subscription and resource group as the project.

Follow the Correct Deployment Flow:

To avoid context mismatches, deploy the model using the recommended navigation path:

Sign in to the Azure AI Foundry portal

Open your project, then navigate to the foundry

Go to Model Catalog → Deployments

Click Create new deployment

Select the model

Provide a unique deployment name

Confirm the deployment type and foundry before submitting

Avoid accessing the Model Catalog via direct URLs or bookmarks, as this can cause the deployment to bind to a previously used foundry.

Check Quota on the Actual Referenced Foundry:

If a quota error occurs, review the foundry name mentioned in the error message. Navigate to that foundry’s Azure OpenAI resource and verify its quota usage. If the foundry is incorrect or not attached to the project, this confirms a foundry mismatch rather than a true quota exhaustion.

Check Quota Limits:

Navigate to the Azure portal.

Go to your Azure OpenAI resource and check your current quota usage.

If you’ve hit a limit, consider submitting a Request for Quota Increase.

Review Previous Deployments:

If you had successful deployments before, compare the settings with the current one to identify discrepancies.

Notes:Azure AI Foundry Model Catalog is not strictly project‑scoped, so it may bind deployments to another compatible Foundry resource, leading to quota errors. There is no UI-based way today to force a single Foundry resource. The only reliable solution is project‑scoped deployment flows or explicit ARM/CLI deployments where the parent Foundry is fixed.

Reference:

https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/create-resource?view=foundry-classic&pivots=web-portal

We hope this information was helpful. If you have any questions, please feel free to contact us.

Thank you
Pulin Gupta 0 Reputation points

2026-02-05T13:41:08.32+00:00
I tried to deploy new model from chat playground of foundry project, as you can see its asking for not enough quota, But I can create models using terraform. If quota was not there, it should not let me create model using following sdk api

"Microsoft.CognitiveServices/accounts/deployments@2024-10-01"
Sridhar M 5,335 Reputation points Microsoft External Staff Moderator

2026-02-06T10:08:05.6+00:00

Hi Pulin Gupta

Change deployment type to Global If data-zone processing is not mandatory, switch the deployment type from Data Zone Standard to Global in the Playground or Terraform. Global deployments usually have higher default quotas and avoid the Data Zone–specific restriction.

Change the upgrade policy from “Upgrade once new default version becomes available” to “Stay on this version.” Then select a model version for which you already have quota. This avoids Foundry’s requirement to have quota for future default versions.

If quota for the newest version is not yet available in your region or data zone, deploy an older supported version that already has quota. Once quota is approved for the new version, you can upgrade safely.
Sridhar M 5,335 Reputation points Microsoft External Staff Moderator

2026-02-09T09:12:51.6933333+00:00

Hi Pulin Gupta

Did you get any chance to review the above response.

Thank you!
Pulin Gupta 0 Reputation points

2026-02-10T09:12:26.6933333+00:00

Change the upgrade policy from “Upgrade once new default version becomes available” to “Stay on this version.” Then select a model version for which you already have quota. This avoids Foundry’s requirement to have quota for future default versions.

This doesnt work, and I have to use datazone standard, I can however deploy using terraform the required quota. So there is some setting for default quota usable per foundry from portal. Do you know where to find that?

2 answers

Your answer

Pulin Gupta 0 Reputation points

2026-02-05T13:41:08.32+00:00

I tried to deploy new model from chat playground of foundry project, as you can see its asking for not enough quota, But I can create models using terraform. If quota was not there, it should not let me create model using following sdk api

"Microsoft.CognitiveServices/accounts/deployments@2024-10-01"
Sridhar M 5,335 Reputation points Microsoft External Staff Moderator

2026-02-06T10:08:05.6+00:00

Hi Pulin Gupta

Change deployment type to Global If data-zone processing is not mandatory, switch the deployment type from Data Zone Standard to Global in the Playground or Terraform. Global deployments usually have higher default quotas and avoid the Data Zone–specific restriction.

Change the upgrade policy from “Upgrade once new default version becomes available” to “Stay on this version.” Then select a model version for which you already have quota. This avoids Foundry’s requirement to have quota for future default versions.

If quota for the newest version is not yet available in your region or data zone, deploy an older supported version that already has quota. Once quota is approved for the new version, you can upgrade safely.
Sridhar M 5,335 Reputation points Microsoft External Staff Moderator

2026-02-09T09:12:51.6933333+00:00

Hi Pulin Gupta

Did you get any chance to review the above response.

Thank you!
Pulin Gupta 0 Reputation points

2026-02-10T09:12:26.6933333+00:00

Change the upgrade policy from “Upgrade once new default version becomes available” to “Stay on this version.” Then select a model version for which you already have quota. This avoids Foundry’s requirement to have quota for future default versions.

This doesnt work, and I have to use datazone standard, I can however deploy using terraform the required quota. So there is some setting for default quota usable per foundry from portal. Do you know where to find that?

Answer 1

Manas Mohanty 14,745 Microsoft External Staff Moderator

Hi Pulin Gupta

It seems the pointers shared could not address the issue.

But I agree with that fact that "If you are trying to deploy a model in not supported region, Azure Logic will trigger a resource creation in supported region, You can submit quota increase requests in other regions".

Attached Region and model documentation of Foundry Azure.

Want to quota that Official Azure Bicep/ARM template might differ from Terraform templates used at customer side sometimes.

Could you please share the details requested in private message to create a support ticket and connect and resolve on call.

Looking forward to hearing from you.

Thank you.

Manas Mohanty 14,745 Reputation points Microsoft External Staff Moderator

2026-02-13T09:40:31.0033333+00:00

Hi Pulin Gupta

Hope you found my correlation between regional support and template selection helpful.

Please share the details requested in private message if you need further assistance.

Thank you
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Manas Mohanty 14,745 Reputation points Microsoft External Staff Moderator

2026-02-16T15:44:25.9033333+00:00

Hi Pulin Gupta

We could not hear from you. If you still need assistance, please share the details via private message.

Thank you.

Answer 2

To ensure that your Azure AI model deployment points to a single AI resource within the correct foundry, you should verify the following steps:

Check Policy Assignment: Ensure that the policy assignment scope includes the correct resource group for your project. The policy should be set to Deny to block any deployments that do not comply with your specified resource.
Model ID Verification: Confirm that the model ID you are using is an exact match, including the version number. This ensures that the deployment is referencing the correct model in the intended foundry.
Role Assignment: Make sure that you have the necessary permissions, such as the Owner or Resource Policy Contributor role, at the target scope to deploy models correctly.
Deployment Configuration: When deploying the model, ensure that you are using the correct deployment name and refer to the model properly in your API calls. Azure OpenAI requires the deployment name rather than just the model name.
Quota Management: If you encounter quota errors, check your quota settings and ensure that you are within the limits for the selected foundry. You may need to manage or increase your quotas as necessary.

By following these steps, you should be able to direct your model deployment to the appropriate AI resource and avoid errors related to incorrect foundry references.

References:

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How to force Azure AI model deployment only point to single AI resource

2 answers

Your answer