@@ -405,6 +405,13 @@ For more information on Provisioned deployments, see our [Provisioned guidance](
 
 This table doesn't include fine-tuning regional availability information.  Consult the [fine-tuning section](#fine-tuning-models) for this information.
 
+### Data zone standard model availability
+
+|Model|United States Data zone regions|European Union Data zone regions|
+|---|---|---|
+| `gpt-4o` (2024-08-06, 2024-05-13)| East US 2 <br> West US 3 <br> | Spain Central <br> West Europe|
+| `gpt-4o-mini` (2024-07-18) | East US 2 <br> West US 3 <br> | Spain Central <br> West Europe|
+
 ### Standard models by endpoint
 
 # [Chat Completions](#tab/standard-chat-completions)
@@ -494,20 +501,7 @@ These models can only be used with Embedding API requests.
 
 ## Fine-tuning models
 
-`gpt-35-turbo` - fine-tuning of this model is limited to a subset of regions, and is not available in every region the base model is available.  
-
-|  Model ID  | Fine-Tuning Regions | Max Request (tokens) | Training Data (up to) |
-|  --- | --- | :---: | :---: |
-| `babbage-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
-| `davinci-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
-| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 4,096 | Sep 2021 |
-| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | Input: 16,385<br> Output: 4,096 |  Sep 2021|
-| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 |
-| `gpt-4` (0613) <sup>**1**</sup> | North Central US <br> Sweden Central | 8192 | Sep 2021 |
-| `gpt-4o-mini` <sup>**1**</sup> (2024-07-18) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 |
-| `gpt-4o` <sup>**1**</sup> (2024-08-06) | East US2 <br> North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 | 
-
-**<sup>1</sup>** GPT-4 is currently in public preview.
+[!INCLUDE [Fine-tune models](../includes/fine-tune-models.md)]
 
 ## Assistants (Preview)
 

@@ -13,18 +13,24 @@ ms.author: mbullwin
 
 # Azure OpenAI deployment types
 
-Azure OpenAI provides customers with choices on the hosting structure that fits their business and usage patterns. The service offers two main types of deployment: **standard** and **provisioned**. Standard is offered with a global deployment option, routing traffic globally to provide higher throughput. Provisioned is also offered with a global deployment option, allowing customers to purchase and deploy provisioned throughput units across Azure global infrastructure. All deployments can perform the exact same inference operations, however the billing, scale and performance are substantially different. As part of your solution design, you will need to make two key decisions:
+Azure OpenAI provides customers with choices on the hosting structure that fits their business and usage patterns. The service offers two main types of deployments: **standard** and **provisioned**. For a given deployment type, customers can align their workloads with their data processing requirements by choosing an Azure geography (`Standard` or `Provisioned`), Microsoft specified data zone (`DataZone-Standard`), or Global (`Global-Standard` or `Global Provisioned-Managed`) processing options.
 
-- **Data processing needs**: global vs. regional resources  
-- **Call volume**: standard vs. provisioned
+All deployments can perform the exact same inference operations, however the billing, scale, and performance are substantially different. As part of your solution design, you will need to make two key decisions:
 
-## Global versus regional deployment types
+- **Data processing location**  
+- **Call volume**
 
-For standard and provisioned deployments, you have an option of two types of configurations within your resource – **global** or **regional**. Global standard is the recommended starting point. 
+## Azure OpenAI Deployment Data Processing Locations
 
-Global deployments leverage Azure's global infrastructure, dynamically route customer traffic to the data center with best availability for the customer’s inference requests. This means you will get the highest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency. For high volume workloads above the specified usage tiers on standard and global standard, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend purchasing provisioned throughput.
+For standard deployments, there are three deployment type options to choose from - global, data zone, and Azure geography. For provisioned deployments, there are two deployment type options to choose from - global and Azure geography. Global standard is the recommended starting point. 
 
-Our global deployments will be the first location for all new models and features. Customers with very large throughput requirements should consider our provisioned deployment offering.
+Global deployments leverage Azure's global infrastructure to dynamically route customer traffic to the data center with the best availability for the customer’s inference requests. This means you will get the highest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency. For high volume workloads above the specified usage tiers on standard and global standard, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend leveraging our provisioned deployment types.
+
+Our global deployments will be the first location for all new models and features. Depending on call volume, customers with large volume and low latency variance requirements should consider our provisioned deployment types.
+
+Data zone deployments leverage Azure's global infrastructure to dynamically route customer traffic to the data center with the best availability for the customer's inference requests within the data zone defined by Microsoft. Positioned between our Azure geography and Global deployment offerings, data zone deployments provide elevated quota limits while keeping data processing within the Microsoft specified data zone. Data stored at rest will continue to remain in the geography of the Azure OpenAI resource (e.g., for an Azure OpenAI resource created in the Sweden Central Azure region, the Azure geography is Sweden).
+
+If the Azure OpenAI resource used in your Data Zone deployment is located in the United States, the data will be processed within the United States. If the Azure OpenAI resource used in your Data Zone deployment is located in a European Union Member Nation, the data will be processed within the European Union Member Nation geographies. For all Azure OpenAI service deployment types, any data stored at rest will continue to remain in the geography of the Azure OpenAI resource. Azure data processing and compliance commitments remain applicable.
 
 ## Deployment types
 
@@ -42,16 +48,6 @@ Azure OpenAI offers three types of deployments. These provide a varied level of
 | **Sku Name in code**     |  `GlobalBatch` |   `GlobalStandard`               |`GlobalProvisionedManaged`| `Standard`   |      `ProvisionedManaged`       |
 | **Billing model**        |  Pay-per-token |Pay-per-token |Hourly billing with optional purchase of monthly or yearly reservations| Pay-per-token |Hourly billing with optional purchase of monthly or yearly reservations|
 
-## Provisioned
-
-Provisioned deployments allow you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it's ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU. Learn more from our [Provisioned throughput concepts article](../concepts/provisioned-throughput.md).
-
-## Standard
-
-Standard deployments provide a pay-per-call billing model on the chosen model. Provides the fastest way to get started as you only pay for what you consume. Models available in each region as well as throughput may be limited.  
-
-Standard deployments are optimized for low to medium volume workloads with high burstiness. Customers with high consistent volume may experience greater latency variability.
-
 ## Global standard
 
 > [!IMPORTANT]
@@ -91,6 +87,24 @@ Key use cases include:
 
 * **Marketing and Personalization:** Generate personalized content and recommendations at scale.
 
+## Data zone standard
+> [!IMPORTANT]
+> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location within the Microsoft specified data zone. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/).
+
+Data zone standard deployments are available in the same Azure OpenAI resource as all other Azure OpenAI deployment types but allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. Data zone standard provides higher default quotas than our Azure geography-based deployment types. 
+
+Customers with high consistent volume may experience greater latency variability. The threshold is set per model. See the [Quotas and limits](/azure/ai-services/openai/quotas-limits#usage-tiers) page to learn more. For workloads that require low latency variance at large volume, we recommend leveraging the provisioned deployment offerings. 
+
+## Standard
+
+Standard deployments provide a pay-per-call billing model on the chosen model. Provides the fastest way to get started as you only pay for what you consume. Models available in each region as well as throughput may be limited.  
+
+Standard deployments are optimized for low to medium volume workloads with high burstiness. Customers with high consistent volume may experience greater latency variability.
+
+## Provisioned
+
+Provisioned deployments allow you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it's ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU. Learn more from our [Provisioned throughput concepts article](../concepts/provisioned-throughput.md).
+
 ### How to disable access to global deployments in your subscription
 
 Azure Policy helps to enforce organizational standards and to assess compliance at-scale. Through its compliance dashboard, it provides an aggregated view to evaluate the overall state of the environment, with the ability to drill down to the per-resource, per-policy granularity. It also helps to bring your resources to compliance through bulk remediation for existing resources and automatic remediation for new resources. [Learn more about Azure Policy and specific built-in controls for AI services](/azure/ai-services/security-controls-policy).

@@ -0,0 +1,29 @@
+---
+title: Fine-tuning model guidance
+titleSuffix: Azure OpenAI
+description: Describes the models that support fine-tuning and the regions where fine-tuning is available.
+author: mrbullwinkle 
+ms.author: mbullwin 
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 10/31/2024
+manager: nitinme
+---
+
+> [!NOTE]
+> `gpt-35-turbo` - Fine-tuning of this model is limited to a subset of regions, and isn't available in every region the base model is available. 
+>
+> The supported regions for fine-tuning might vary if you use Azure OpenAI models in an AI Studio project versus outside a project.
+
+|  Model ID  | Fine-tuning regions | Max request (tokens) | Training Data (up to) |
+|  --- | --- | :---: | :---: |
+| `babbage-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
+| `davinci-002` | North Central US <br> Sweden Central  <br> Switzerland West | 16,384 | Sep 2021 |
+| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 4,096 | Sep 2021 |
+| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | Input: 16,385<br> Output: 4,096 |  Sep 2021|
+| `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 |
+| `gpt-4` (0613) <sup>**1**</sup> | North Central US <br> Sweden Central | 8192 | Sep 2021 |
+| `gpt-4o-mini` <sup>**1**</sup> (2024-07-18) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 |
+| `gpt-4o` <sup>**1**</sup> (2024-08-06) | East US2 <br> North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 | 
+
+**<sup>1</sup>** GPT-4 is currently in public preview.

@@ -19,6 +19,8 @@ ms.custom: include, build-2024
 - An [Azure AI hub resource](../../../ai-studio/how-to/create-azure-ai-resource.md).
 - An [Azure AI project](../../../ai-studio/how-to/create-projects.md) in Azure AI Studio.
 - An [Azure OpenAI connection](/azure/ai-studio/how-to/connections-add?tabs=azure-openai#connection-details) to a resource in a [region where fine-tuning is supported](/azure/ai-services/openai/concepts/models#fine-tuning-models).
+    > [!NOTE]
+    > The supported regions might vary if you use Azure OpenAI models in an AI Studio project versus outside a project.
 - Fine-tuning access requires **Cognitive Services OpenAI Contributor** role on the Azure OpenAI resource.
 - If you don't already have access to view quota and deploy models in Azure AI Studio you need [more permissions](../how-to/role-based-access-control.md).
 
@@ -92,7 +94,7 @@ In addition to the JSONL format, training and validation data files must be enco
 
 ### Create your training and validation datasets
 
-The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number are not enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful.
+The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number is not enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful.
 
 In general, doubling the dataset size can lead to a linear increase in model quality. But keep in mind, low quality examples can negatively impact performance. If you train the model on a large amount of internal data, without first pruning the dataset for only the highest quality examples you could end up with a model that performs much worse than expected.
 

@@ -108,6 +108,18 @@ The following sections provide you with a quick guide to the default quotas and
 
 M = million | K = thousand
 
+### gpt-4o data zone standard
+
+| Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |
+|---|---|:---:|:---:|
+|`gpt-4o`|Enterprise agreement | 10 M | 60 K |
+|`gpt-4o-mini` | Enterprise agreement | 20 M | 120 K |
+|`gpt-4o` |Default | 300 K | 1.8 K |
+|`gpt-4o-mini` | Default | 1 M | 6 K  |
+
+M = million | K = thousand
+
+
 ### gpt-4o standard
 
 | Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |
@@ -121,14 +133,14 @@ M = million | K = thousand
 
 #### Usage tiers
 
-Global Standard deployments use Azure's global infrastructure, dynamically routing customer traffic to the data center with best availability for the customer’s inference requests. This enables more consistent latency for customers with low to medium levels of traffic. Customers with high sustained levels of usage might see more variability in response latency.
+Global standard deployments use Azure's global infrastructure, dynamically routing customer traffic to the data center with best availability for the customer’s inference requests. Similarly, Data zone standard deployments allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. This enables more consistent latency for customers with low to medium levels of traffic. Customers with high sustained levels of usage might see more variability in response latency.
 
 The Usage Limit determines the level of usage above which customers might see larger variability in response latency. A customer’s usage is defined per model and is the total tokens consumed across all deployments in all subscriptions in all regions for a given tenant.
 
 > [!NOTE]
-> Usage tiers only apply to standard and global standard deployment types. Usage tiers do not apply to global batch and provisioned throughput deployments.
+> Usage tiers only apply to standard, data zone standard, and global standard deployment types. Usage tiers do not apply to global batch and provisioned throughput deployments.
 
-#### GPT-4o global standard & standard
+#### GPT-4o global standard, data zone standard, & standard
 
 |Model| Usage Tiers per month |
 |----|----|

@@ -20,6 +20,11 @@ This article provides a summary of the latest releases and major documentation u
 
 ## October 2024
 
+### NEW data zone standard deployment type
+Data zone standard deployments are available in the same Azure OpenAI resource as all other Azure OpenAI deployment types but allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. Data zone standard provides higher default quotas than our Azure geography-based deployment types.  Data zone standard deployments are supported on `gpt-4o-2024-08-06`, `gpt-4o-2024-05-13, and `gpt-4o-mini-2024-07-18` models.
+
+For more information, see the [deployment types guide](https://aka.ms/aoai/docs/deployment-types).
+
 ### Global Batch GA
 
 Azure OpenAI global batch is now generally available.