> ## Documentation Index > Fetch the complete documentation index at: https://hobbyist-e43fa225.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart: Create a provisioned throughput deployment > Create a provisioned throughput deployment in Microsoft Foundry, make an inference call, and verify utilization. **Currently viewing:** **New Foundry portal version** - [Switch to version for the classic Foundry portal](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/provisioned-quickstart) In this quickstart, you create a provisioned throughput deployment in Microsoft Foundry, make an inference call to confirm it works, and view its utilization metric. A provisioned throughput deployment gives your application dedicated model processing throughput with predictable latency. Billing is done per provisioned throughput unit (PTU) per hour. For long-term workloads, Azure Reservations offer financial discounts compared to hourly billing. For a full conceptual introduction, see [What is provisioned throughput for Foundry Models?](../concepts/provisioned-throughput). ## Prerequisites * An Azure subscription with a valid payment method. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin. * **Azure Contributor** or **Cognitive Services Contributor** role on the subscription or resource group where you plan to create the deployment. * A [Microsoft Foundry project](../../how-to/create-projects) in the region where you have PTU quota. A Foundry project is managed under a Foundry resource. * Optionally, for deployment using Azure CLI, have [Azure CLI installed](https://learn.microsoft.com/cli/azure/install-azure-cli). ## Check model and region availability Before creating a deployment, confirm that your model supports provisioned throughput in your target region. 1. Go to the [model and region availability table](../../foundry-models/concepts/models-sold-directly-by-azure-region-availability) to see if your model supports provisioned throughput deployment in your target region. 2. Filter by your region and verify that the model appears in a **Provisioned** deployment type. Also note the model's minimum PTU count, as you need this information when you configure the deployment. Minimums vary by model and are listed in [Deployment parameters and throughput values by model](../how-to/provisioned-throughput-sizing#deployment-parameters-and-throughput-values-by-model). ## Check PTU quota Before following this quickstart, check that you have quota for your target region and deployment type. To check your quota: 1. Sign in to [Microsoft Foundry](https://ai.azure.com/?cid=learnDocs). Make sure the **New Foundry** toggle is on. These steps refer to **Foundry (new)**. 1. Select the subscription and the Foundry resource in the region where you have PTU quota. 2. Select **Operate** in the upper-right navigation, then select **Quota** in the left pane. 3. Select **Provisioned throughput unit** to see your available quota. If you don't have quota, select **Request Quota** and complete the form. Quota approval can take several days, and you receive an email notification when the request is approved. You can also follow this [direct link to the quota request form](https://aka.ms/oai/stuquotarequest). ## Create a provisioned deployment In this section, you create a provisioned deployment using the Foundry portal or the Azure CLI. ### Use the Foundry portal for deployment 1. Select **Discover** in the upper-right navigation, then select **Models** in the left pane. 2. Select the model you want to deploy to open its model card, such as `gpt-5.1`. 3. Select **Deploy** > **Custom settings**. 4. In the **Deployment type** dropdown, select a provisioned deployment type: **Global Provisioned Throughput**, **Data Zone Provisioned Throughput**, or **Regional Provisioned Throughput**. 5. Fill in the deployment fields: | Field | Description | | -------------------------------- | -------------------------------------------------------------------------- | | **Deployment name** | A name you choose. Use this name in your code to call the model. | | **Model** | The model to deploy, e.g., `gpt-5.1`. | | **Model version** | The version of the model. | | **Provisioned throughput units** | The number of PTUs to allocate. Must meet the model's minimum, e.g., `50`. | 6. Select **Confirm pricing** to review the hourly rate for the deployment. **Billing starts immediately the deployment is created, even when no requests are being sent**. You stop billing by deleting your deployment. If you're unsure of the costs, select **Cancel** and review [PTU billing and cost management](/models/provisioned-throughput-billing) before continuing. 7. Confirm and create the deployment. ### (Optional) Use the Azure CLI for deployment Alternatively, you can create your deployment by using the Azure CLI. 1. Create a provisioned deployment for GPT-5.1 with a PTU count of 50 PTUs. ```azurecli theme={null} az cognitiveservices account deployment create \ --name \ --resource-group \ --deployment-name \ --model-name GPT-5.1 \ --model-version "2025-11-13" \ --model-format OpenAI \ --sku-capacity 50 \ --sku-name GlobalProvisionedManaged ``` * Replace ``, ``, `` with your values. * `--sku-name` specifies the deployment type: `GlobalProvisionedManaged`, `DataZoneProvisionedManaged`, or `ProvisionedManaged`. * `--sku-capacity` is the number of PTUs. Here, it's set to 50. Reference: [az cognitiveservices account deployment](https://learn.microsoft.com/cli/azure/cognitiveservices/account/deployment) 2. Confirm that the deployment completed successfully: ```azurecli theme={null} az cognitiveservices account deployment show \ --deployment-name \ --name \ --resource-group \ --query "properties.provisioningState" -o tsv ``` The output should display `Succeeded`. The model is ready to use after provisioning completes. Reference: [az cognitiveservices account deployment show](https://learn.microsoft.com/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-show) REST, ARM template, Bicep, and Terraform can also be used to create deployments. See [Automate deployments](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/quota) and replace `sku.name` with `GlobalProvisionedManaged`, `DataZoneProvisionedManaged`, or `ProvisionedManaged`. ## Make an inference call The inference code for a provisioned deployment is the same as for any other deployment type. Use your deployment name (not the model name) as the `model` parameter value. The code in this section uses API key authentication. You can also use Entra ID authentication. For details on using Entra ID authentication when making an inference call, see [How to generate text responses with Microsoft Foundry Models](../../foundry-models/how-to/generate-responses). Before running the sample, set the following environment variable: * `AZURE_OPENAI_API_KEY`: your resource API key. Don't hard-code credentials in your application. For production workloads, use a secure credential store such as [Azure Key Vault](https://learn.microsoft.com/azure/key-vault/general/overview). See [Security features for Azure AI services](../../../ai-services/security-features). 1. Install the OpenAI SDK: ```bash theme={null} pip install openai ``` 2. Configure the OpenAI client, specify your deployment, and generate responses. Replace `` with your Foundry resource name. ```python theme={null} import os from openai import OpenAI client = OpenAI( api_key=os.getenv("AZURE_OPENAI_API_KEY"), base_url="https://.openai.azure.com/openai/v1/", ) response = client.responses.create( model="", # Your deployment name, not the model name input="What is provisioned throughput?", max_output_tokens=100, ) print(response.output_text) ``` Send a POST request to the Responses API endpoint. Replace `` with your Foundry resource name and `` with your deployment name. ```bash theme={null} curl https://.openai.azure.com/openai/v1/responses \ -H "Content-Type: application/json" \ -H "api-key: $AZURE_OPENAI_API_KEY" \ -d '{ "model": "", "input": "What is provisioned throughput?", "max_output_tokens": 100 }' ``` ## View deployment utilization After making calls, confirm that traffic is reaching your deployment by checking its utilization in the Azure portal. 1. Sign in to the [Azure portal](https://portal.azure.com). 2. Navigate to your Foundry resource and select **Metrics** in the left navigation. 3. Select the **Provisioned-managed utilization V2** metric. 4. If you have more than one deployment in the resource, filter by the deployment name to view utilization per deployment. A utilization reading near 0% immediately after your test call is normal — the metric updates on a monitoring window. Screenshot of Azure Metrics showing Provisioned-managed Utilization V2 chart filtered by deployment name. For a full explanation of how utilization is calculated and what to do when it reaches 100%, see [Operate provisioned deployments in production](../how-to/provisioned-get-started#measure-deployment-utilization). ## Consider setting up spillover Spillover automatically routes overflow requests from your provisioned deployment to a standard deployment in the same Foundry resource. When your provisioned deployment is fully utilized and returns a `429` code, spillover redirects those excess requests to the standard deployment instead of failing them, helping reduce disruptions during traffic bursts. To learn more about enabling spillover and monitoring spillover requests, see [Manage traffic with spillover for provisioned deployments](../how-to/spillover-traffic-management). ## Consider purchasing a reservation Your deployment is billed at the hourly rate. If you plan to keep it running for more than a few days, purchasing an **Azure Reservation** reduces your effective \$/PTU/hr cost compared to hourly billing. If you plan to purchase a reservation after creating your deployment, verify that you have the **owner role** or **reservation purchaser role** on an Azure subscription. The role needed to purchase reservations differs from the role needed to create deployments. See [Provisioned Throughput reservations](https://aka.ms/oai/docs/ptum-reservations) for role requirements. Always create and confirm your deployment before purchasing a reservation. The reservation must match your deployment's type (Global, Data Zone, or Regional) and subscription scope. For Data Zone and Regional deployments, the reservation region must also match. For Global deployments, a single Global reservation can cover Global PTU deployments across multiple regions. Committing to a reservation for capacity you haven't confirmed is available can result in a financial commitment you can't use. For sizing guidance, purchase steps, and management, see [Azure Reservations for provisioned throughput](../concepts/provisioned-throughput-billing#azure-reservations-for-provisioned-throughput). ## Clean up resources Deleting the Foundry resource doesn't automatically delete its deployments. Always delete all deployments before deleting the resource, as charges for deployments on a deleted resource continue until the resource is purged. See [Clean up resources](../how-to/provisioned-get-started#clean-up-resources). Deleting a deployment doesn't cancel an Azure Reservation. If you purchased one, cancel or exchange it separately on the [Reservations page in the Azure portal](https://portal.azure.com/#view/Microsoft_Azure_Reservations/ReservationsBrowseBlade/productType/Reservations). Cancellation might incur an early termination fee. Follow these steps to stop hourly billing by deleting the deployment. ### Delete deployment in the Foundry portal 1. In the [Foundry portal](https://ai.azure.com/?cid=learnDocs), navigate to your deployments. 2. Select the deployment, then select **Delete** and confirm. ### (Optional) Delete deployment with the Azure CLI ```azurecli theme={null} az cognitiveservices account deployment delete \ --deployment-name \ --name \ --resource-group ``` Reference: [az cognitiveservices account deployment delete](https://learn.microsoft.com/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-delete) ## Next step * [What is provisioned throughput for Foundry Models?](../concepts/provisioned-throughput)