Microsoft Foundry gpt-5-mini deployment blocked with error 715-123420

Hello Kishore,

Greetings! Thanks for raising this question in the Q&A forum.

Error 715-123420 is not a quota, region availability, or model-specific issue. It comes from Microsoft's automated fraud and abuse detection system that sits in front of Azure OpenAI and Microsoft Foundry deployments, and it flags at the subscription level rather than the resource or model level. This is why the message explicitly points you to contact support rather than offering a self-service fix, since only the backend review team can clear it. Newer subscriptions, recent resource churn (creating, deleting, or recreating Foundry resources across regions), or a spike in deployment attempts in a short window are the most common triggers.

Here is how to resolve it:

1. Open an Azure support request through the Azure portal under Help + Support, or at https://azure.microsoft.com/support/create-ticket/. Select Subscription Management as the issue type, since this is an account-level risk flag rather than a technical bug in the Foundry service itself.
2. Include your subscription ID, the Foundry resource name, region (Sweden Central), model name and version (gpt-5-mini, 2025-08-07), and the exact error code 715-123420 in the ticket so the reviewing team can locate the specific flagged event.
3. Capture and include the Trace ID, Client request ID, and Service request ID if the error response includes them, since these speed up backend triage significantly. If your portal or CLI output does not show them, check the raw response body of the deployment call or the browser network tab when reproducing the error.
4. Hold off on repeated deployment attempts while the ticket is open. Retrying repeatedly against the same subscription can reinforce the flag rather than help.
5. This type of subscription unblock request generally does not require a paid technical support plan, since it is a billing and subscription matter rather than a product defect. If a support agent tries to route you to a paid plan, ask them to redirect the case to Subscription and Billing support instead.
6. Resolution typically takes a few business days once the ticket is filed and reviewed, though there is no published SLA for this specific control.

If this answer helps you kindly accept the answer which will help others who have similar questions.

Best Regards,

Jerald Felix.

Hello @kishore kumar ,

Welcome to Microsoft Q&A .Thank you for reaching out to us.

The current behavior suggests that the deployment request may be failing during an earlier validation stage before the model provisioning process begins. The error message itself does not expose the exact validation reason, so confirmation requires additional backend investigation.

Possible areas that may require review include:

• Subscription eligibility validation
• Subscription-level authorization checks
• Automated risk or trust validation controls
• Model deployment access validation

Regarding the request for an RTFP review, the available information does not conclusively confirm that an RTFP restriction is present. However, the observed pattern is consistent with scenarios where additional backend validation may be required to determine whether a subscription-level condition is preventing deployments from completing.

The following checks can help rule out common deployment blockers before backend investigation.

1. Confirming Model Availability Verify that the selected models are supported in the intended region and deployment type. Checks:
   • Confirm model availability for the selected Azure region.
   • Confirm that the selected deployment type (for example, Global Standard) is supported for the model.
2. Validating Quota and Capacity Availability Confirm that sufficient quota is available for:
   • Subscription
   • Region
   • Selected model deployment
   Quota or capacity issues normally return more specific quota-related messages. However, validating quota helps eliminate standard deployment limitations.
3. Verifying Resource Provider Registration Confirm that the required resource providers are registered and in a healthy state:
   • Microsoft.CognitiveServices
   • Microsoft.MachineLearningServices
   Expected status: Registered
4. Reviewing Governance and Policy Restrictions Review Azure Policy assignments that may restrict:
   • AI model deployments
   • Allowed regions
   • Resource types
   • Deployment configurations
5. Performing Additional Deployment Validation To determine whether the issue is limited to a specific deployment method or occurs at the service level:
   • Retry after a short interval to rule out temporary service behavior.
   • Attempt a deployment with a new deployment name to exclude deployment-specific inconsistencies.
   • Test deployment through Azure AI Foundry portal.
   • If possible, test through Azure CLI or ARM/Bicep to confirm whether the failure occurs at the service layer.

As an additional verification , please note that

For sponsored subscriptions, it may also be useful to confirm that the selected models appear under the appropriate model collection in Azure AI Foundry, as model eligibility and billing behavior can vary depending on the model source.

The following references might be helpful , please check them out

• Region availability for Foundry Models sold by Azure - Microsoft Foundry | Microsoft Learn
• Azure OpenAI in Microsoft Foundry Models Quotas and Limits - Microsoft Foundry | Microsoft Learn
• Azure resource providers and types - Azure Resource Manager | Microsoft Learn
• Overview of Azure Policy - Azure Policy | Microsoft Learn
• az cognitiveservices account deployment | Microsoft Learn
• Sponsorship Coverage for Microsoft Foundry Models | Microsoft Learn

We have reached out to you on private messenger for further assistance

Thank you