Merge pull request #4835 from ssalgadodev/rebrand-for-standard

Rebrand for pay as you go to standard

Author: ShannonLeavitt

articles/ai-foundry/concepts/models-featured.md

@@ -250,8 +250,8 @@ See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure
 
 Mistral AI offers two categories of models, namely: 
 
-- _Premium models_: These include Mistral Large, Mistral Small, Mistral-OCR-2503, and Ministral 3B models, and are available as standard deployments with serverless pay per token offer.  
-- _Open models_: These include Mistral-small-2503, Codestral, and Mistral Nemo (that are available as standard deployments with serverless pay per token offer), and [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](../how-to/deploy-models-mistral-open.md)(that are available to download and run on self-hosted managed endpoints).
+- _Premium models_: These include Mistral Large, Mistral Small, Mistral-OCR-2503, and Ministral 3B models, and are available as standard deployments.   
+- _Open models_: These include Mistral-small-2503, Codestral, and Mistral Nemo (that are available as standard deployments), and [Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01](../how-to/deploy-models-mistral-open.md)(that are available to download and run on self-hosted managed endpoints).
 
 
 | Model  | Type | Capabilities |

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -151,7 +151,7 @@ For more information, see the [Azure pricing calculator](https://azure.microsoft
 
 ### Monitor costs for models offered through the Azure Marketplace
 
-Models deployed as a service using pay-as-you-go are offered through the Azure Marketplace. The model publishers might apply different costs depending on the offering. Each project in Azure AI Foundry portal has its own subscription with the offering, which allows you to monitor the costs and the consumption happening on that project. Use [Microsoft Cost Management](https://azure.microsoft.com/products/cost-management) to monitor the costs:
+Standard deployments are offered through the Azure Marketplace. The model publishers might apply different costs depending on the offering. Each project in Azure AI Foundry portal has its own subscription with the offering, which allows you to monitor the costs and the consumption happening on that project. Use [Microsoft Cost Management](https://azure.microsoft.com/products/cost-management) to monitor the costs:
 
 1. Sign in to [Azure portal](https://portal.azure.com).
 
@@ -209,7 +209,7 @@ Azure AI services run on Azure infrastructure that accrues costs along with Azur
 
 When you create or use Azure AI services resources, you might get charged based on the services that you use. There are two billing models available for Azure AI services: 
 
-- Pay-as-you-go: Pay-as-you-go pricing, you're billed according to the Azure AI services offering that you use, based on its billing information.
+- Standard: Standard pricing, you're billed according to the Azure AI services offering that you use, based on its billing information.
 - Commitment tiers: With commitment tier pricing, you commit to using several service features for a fixed fee, enabling you to have a predictable total cost based on the needs of your workload. You're billed according to the plan you choose. See [Quickstart: purchase commitment tier pricing](../../ai-services/commitment-tier.md) for information on available services, how to sign up, and considerations when purchasing a plan.
 
 > [!NOTE]

articles/ai-foundry/how-to/deploy-models-gretel-navigator.md

@@ -55,7 +55,7 @@ To use Gretel Navigator chat model with Azure AI Foundry, you need the following
 
 **Deployment to standard deployments**
 
-Gretel Navigator chat model can be deployed to standard deployment with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+Gretel Navigator chat model can be deployed to standard deployment. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
 
 Deployment to a standard deployment doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a standard deployment](deploy-models-serverless.md).
 
@@ -301,7 +301,7 @@ To use Gretel Navigator chat model with Azure AI Foundry, you need the following
 
 **Deployment to standard deployments**
 
-Gretel Navigator chat model can be deployed to standard deployments with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
+Gretel Navigator chat model can be deployed to standard deployments. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. 
 
 Deployment to a standard deployment doesn't require quota from your subscription. If your model isn't deployed already, use the Azure AI Foundry portal, Azure Machine Learning SDK for Python, the Azure CLI, or ARM templates to [deploy the model as a standard deployment](deploy-models-serverless.md).
 

articles/ai-foundry/how-to/deploy-models-mistral-open.md

@@ -21,8 +21,8 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 In this article, you learn about Mistral-7B and Mixtral chat models and how to use them.
 Mistral AI offers two categories of models, namely:
 
-- _Premium models_: These include [Mistral Large, Mistral Small, and Ministral 3B](deploy-models-mistral.md) models, and are available as standard deployments with pay-as-you-go token-based billing.  
-- _Open models_: These include [Codestral](deploy-models-mistral-codestral.md) and [Mistral Nemo](deploy-models-mistral-nemo.md) (that are available as standard deployments with pay-as-you-go token-based billing), and Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01 (that are available to download and run on self-hosted managed endpoints).
+- _Premium models_: These include [Mistral Large, Mistral Small, and Ministral 3B](deploy-models-mistral.md) models, and are available as standard deployments with Standard billing.  
+- _Open models_: These include [Codestral](deploy-models-mistral-codestral.md) and [Mistral Nemo](deploy-models-mistral-nemo.md) (that are available as standard deployments), and Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01 (that are available to download and run on self-hosted managed endpoints).
 
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]

articles/ai-foundry/how-to/deploy-models-serverless-availability.md

@@ -22,11 +22,11 @@ In this article, you learn about which regions are available for each of the mod
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]
 
-Certain models in the model catalog can be deployed as a standard deployment with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
+Certain models in the model catalog can be deployed as a standard deployment. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
 ## Region availability
 
-Pay-as-you-go billing is available only to users whose Azure subscription belongs to a billing account in a country/region where the model provider has made the offer available (see "offer availability region" in the table in the next section). If the offer is available in the relevant region, the user then must have a Hub/Project in the Azure region where the model is available for deployment or fine-tuning, as applicable (see "Hub/Project Region" columns in the following tables).
+Standard billing is available only to users whose Azure subscription belongs to a billing account in a country/region where the model provider has made the offer available (see "offer availability region" in the table in the next section). If the offer is available in the relevant region, the user then must have a Hub/Project in the Azure region where the model is available for deployment or fine-tuning, as applicable (see "Hub/Project Region" columns in the following tables).
 
 > [!NOTE]
 > If you plan to access your deployed models in Azure Machine Learning Studio, you must use a **[!INCLUDE [hub](../includes/hub-project-name.md)]**, rather than a **[!INCLUDE [fdp](../includes/fdp-project-name.md)]**. For more information, see [Project types](../what-is-azure-ai-foundry.md#project-types).

articles/ai-foundry/how-to/deploy-models-serverless.md

@@ -15,11 +15,11 @@ ms.custom: build-2024, serverless, devx-track-azurecli, ignite-2024
 
 # Deploy models as standard deployments
 
-In this article, you learn how to deploy a model from the model catalog as a standard deployment with pay-as-you-go token based billing.
+In this article, you learn how to deploy a model from the model catalog as a standard deployment.
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]
 
-[Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a standard deployments with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
+[Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a standard deployments. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
 
 This article uses a Meta Llama model deployment for illustration. However, you can use the same steps to deploy any of the [models in the model catalog that are available for standard deployment](deploy-models-serverless-availability.md).
 

articles/ai-foundry/how-to/deploy-nvidia-inference-microservice.md

@@ -47,9 +47,9 @@ NVIDIA inference microservices are containers built by NVIDIA for optimized pret
         -    Microsoft.MachineLearningServices/workspaces/onlineEndpoints/* 
 
 
-## NVIDIA NIM pay-as-you-go offer on Azure Marketplace by NVIDIA
+## NVIDIA NIM Standard deployment on Azure Marketplace by NVIDIA
 
- NVIDIA NIMs available on Azure AI Foundry model catalog can be deployed with a pay-as-you-go subscription to the [NVIDIA NIM SaaS offer](https://aka.ms/nvidia-nims-plan) on Azure Marketplace. This offer includes a 90-day trial and a pay-as-you-go price of $1 per GPU hour post the trial period. The trial applies to all NIMs associated with a particular SaaS subscription, and starts from the time the SaaS subscription was created. SaaS subscriptions scope to an Azure AI Foundry project, so you have to subscribe to the NIM offer only once within a project, then you are able to deploy all NIMs offered by NVIDIA in the AI Foundry model catalog. If you want to deploy NIM in a different project with no existing SaaS subscription, you will have to resubscribe to the offer.  
+ NVIDIA NIMs available on Azure AI Foundry model catalog can be deployed with a Standard subscription to the [NVIDIA NIM SaaS offer](https://aka.ms/nvidia-nims-plan) on Azure Marketplace. This offer includes a 90-day trial and a Standard price of $1 per GPU hour post the trial period. The trial applies to all NIMs associated with a particular SaaS subscription, and starts from the time the SaaS subscription was created. SaaS subscriptions scope to an Azure AI Foundry project, so you have to subscribe to the NIM offer only once within a project, then you are able to deploy all NIMs offered by NVIDIA in the AI Foundry model catalog. If you want to deploy NIM in a different project with no existing SaaS subscription, you will have to resubscribe to the offer.  
 
  Azure AI Foundry enables a seamless purchase experience of the NVIDIA NIM offering on Marketplace from the NVIDIA collection in the model catalog, and further deployment on managed compute.
 

articles/ai-foundry/how-to/fine-tune-serverless.md

@@ -20,7 +20,7 @@ zone_pivot_groups: azure-ai-model-fine-tune
 
 Azure AI Foundry enables you to customize large language models to your specific datasets through a process called fine-tuning. This process offers significant benefits by allowing for customization and optimization tailored to specific tasks and applications. The advantages include improved performance, cost efficiency, reduced latency, and tailored outputs.
 
-**Cost Efficiency**: Azure AI Foundry's fine-tuning can be more cost-effective, especially for large-scale deployments, thanks to pay-as-you-go pricing.
+**Cost Efficiency**: Azure AI Foundry's fine-tuning can be more cost-effective, especially for large-scale deployments, thanks to Standard pricing.
 
 **Model Variety**: Azure AI Foundry's standard deployment fine-tuning  offers support for both proprietary and open-source models, providing users with the flexibility to select the models that best suit their needs without being restricted to a single type.
 
@@ -122,7 +122,7 @@ Azure AI Foundry portal provides the Create custom model wizard, so you can inte
 
 1. Choose the model you want to fine-tune from the Azure AI Foundry [model catalog](https://ai.azure.com/explore/models).
 2. On the model's **Details page**, select **fine-tune**. Some foundation models support both **standard deployment** and **Managed compute**, while others support one or the other.
-3. If you're presented the options for **standard deployment** and [**Managed compute**](./fine-tune-managed-compute.md), select **standard deployment** for fine-tuning. This action opens up a wizard that shows information about **pay-as-you-go** fine-tuning for your model.
+3. If you're presented the options for **standard deployment** and [**Managed compute**](./fine-tune-managed-compute.md), select **standard deployment** for fine-tuning. This action opens up a wizard that shows information about **Standard** fine-tuning for your model.
 
 ### Choose your training data
 The next step is to either choose existing prepared training data or upload new prepared training data to use when customizing your model. The **Training data** pane displays any existing, previously uploaded datasets and also provides options to upload new training data.
@@ -754,7 +754,7 @@ The training data used is the same as demonstrated in the SDK notebook. The CLI
 
 ## Content filtering
 
-Models deployed as a service with pay-as-you-go billing are protected by Azure AI Content Safety. When deployed to real-time endpoints, you can opt out of this capability. With Azure AI Content Safety enabled, both the prompt and completion pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Learn more about [Azure AI Content Safety](../concepts/content-filtering.md).
+Standard deployment models are protected by Azure AI Content Safety. When deployed to real-time endpoints, you can opt out of this capability. With Azure AI Content Safety enabled, both the prompt and completion pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Learn more about [Azure AI Content Safety](../concepts/content-filtering.md).
 
 
 ## Next steps

articles/ai-foundry/how-to/quota.md

@@ -58,7 +58,7 @@ A quota is a credit limit on Azure resources, not a capacity guarantee. If
 > [!NOTE]
 > Azure AI Foundry compute has a separate quota from the core compute quota. 
 
-Default limits vary by offer category type, such as free trial, pay-as-you-go, and virtual machine (VM) series (such as Dv2, F, and G). 
+Default limits vary by offer category type, such as free trial, standard deployment, and virtual machine (VM) series (such as Dv2, F, and G). 
 
 ## Azure AI Foundry quota 
 

articles/ai-foundry/model-inference/concepts/models.md

@@ -38,7 +38,7 @@ Explore the following model families available:
 Model availability varies by model provider, deployment SKU, and cloud. All models available in Azure AI Foundry Models support the [Global standard](deployment-types.md#global-standard) deployment type which uses global capacity to guarantee throughput. [Azure OpenAI models](#azure-openai) also support regional deployments and [sovereign clouds](/entra/identity-platform/authentication-national-cloud)—Azure Government, Azure Germany, and Azure China 21Vianet.
 
 > [!TIP]
-> The Azure AI model catalog offers a larger selection of models, from a bigger range of providers. However, those models might require you to host them on your infrastructure, including the creation of an AI hub and project. Azure AI model service provides a way to consume the models as APIs without hosting them on your infrastructure, with a pay-as-you-go billing. Learn more about the [Azure AI model catalog](../../../ai-studio/how-to/model-catalog-overview.md).
+> The Azure AI model catalog offers a larger selection of models, from a bigger range of providers. However, those models might require you to host them on your infrastructure, including the creation of an AI hub and project. Azure AI model service provides a way to consume the models as APIs without hosting them on your infrastructure, with a Standard billing. Learn more about the [Azure AI model catalog](../../../ai-studio/how-to/model-catalog-overview.md).
 
 ### AI21 Labs