Azure OpenAI Regions Overview

Azure OpenAI Regions are strategically located across the globe to provide low-latency access to AI services.

There are currently 12 Azure OpenAI Regions, with new ones being added regularly.

Each Region is designed to support a wide range of AI workloads, from text and speech generation to image and video processing.

These Regions are deployed in major metropolitan areas, ensuring high-speed connectivity and reduced latency for users.

A unique perspective: Latency between Azure Regions

Azure OpenAI regions offer a range of deployment options, making it easy to find the right fit for your needs.

The availability of models varies by region, with some models available in multiple regions, such as Mistral Large, which is available in multiple regions, providing high performance for demanding applications.

You can try the o1-preview and o1-mini models in the early access playground in the East US2 region, but registration is required, and access will be granted based on Microsoft’s eligibility criteria.

To deploy the GPT-4o mini model, you can choose from several regions, including Canada East, East US, East US2, North Central US, and Sweden Central.

A fresh viewpoint: Azure Region Pairing

GPT-4o mini is available for standard and global standard deployment in the East US and Sweden Central regions, and for global batch deployment in East US, Sweden Central, and West US regions.

The model is currently available for both standard and global standard deployment in the East US region, making it a convenient option for many users.

Here's a list of regions where you can deploy the GPT-4o model for global standard deployment:

Azure OpenAI regions offer a range of benefits, including efficient model deployment and optimal performance across various locations.

The performance of Azure OpenAI models can vary significantly based on the region in which they are deployed, with accuracy, response time, and user satisfaction being key metrics.

GPT-4o is now available for global standard deployments in 22 regions, including AustraliaEast, BrazilSouth, and EastUS, making it easier to deploy models across different locations.

Here's a summary of the regions where GPT-4o is available:

This expansion of regions makes it easier to deploy GPT-4o across different locations, improving performance and accessibility.

Regional Data Support is a crucial aspect of Azure OpenAI On Your Data. You can now use Azure OpenAI On Your Data in the South Africa North region.

Azure OpenAI On Your Data has expanded its regional support to include the South Africa North region. This means you can take advantage of its features and capabilities in this new location.

The GPT-4 (0125) model is available for use in regions with Azure OpenAI On Your Data. This model can be used in conjunction with the expanded regional support.

For those interested in trying the o1-preview and o1-mini models, they are available in the East US2 region through the AI Foundry early access playground. Registration is required for access.

To access the o1-preview and o1-mini models, you'll need to follow these steps: navigate to https://ai.azure.com/resources and select a resource in the eastus2 region, then select Early access playground (preview) from the upper left-hand panel.

Worth a look: Azure Openai Completions Playground for Gpt-4o

GPT-4o mini is available in several regions for standard and global standard deployment. Here are the regions where GPT-4o mini is available:

GPT-4o mini's regional availability makes it a versatile option for users. Its availability in multiple regions can help you choose the best deployment option for your needs.

Take a look at this: Windows Azure High Availability

The performance of Azure OpenAI models varies significantly based on the region in which they are deployed. This is evident in the comparative analysis of Azure OpenAI models by region.

In North America, GPT-4 achieved an accuracy of 92.5% and a response time of 150ms. In contrast, GPT-3.5-turbo in South America had an accuracy of 89.0% and a response time of 200ms.

The table below summarizes the performance of different models across several regions:

GPT-4o mini is available for standard and global standard deployment in the East US and Sweden Central regions.

GPT-4o is now available for global standard deployments in 18 regions, including AustraliaEast, BrazilSouth, and JapanEast.

The expansion of regions available for global standard deployments of GPT-4o is a significant development, allowing for more flexible and efficient model deployment across different locations.

Here are the regions where GPT-4o is now available for global standard deployments:

This expansion is part of Azure OpenAI's ongoing efforts to improve its global deployment capabilities and make its models more accessible to developers and businesses worldwide.

As you explore Azure OpenAI regions, you'll want to know about the performance and quotas that come with each region. Regional quota limits increase for certain models and regions, allowing you to take advantage of higher Tokens per minute (TPM) by migrating workloads to these models and regions.

With increased quota limits, you can process more data and get more work done in less time. This is especially useful for large-scale projects or applications that require high processing power.

To give you a better idea of the increased quota limits, here's a brief overview:

The latest GPT-4o model has been released in the early access playground, offering enhanced capabilities and a significant increase in output tokens.

GPT-4o 2024-08-06 now supports complex structured outputs, a feature that was previously lacking in earlier versions.

One notable upgrade is the increase in max output tokens from 4,096 to 16,384, which will be beneficial for users who require more output from the model.

Azure customers can test out GPT-4o 2024-08-06 in the AI Foundry early access playground, which doesn't require a specific Azure resource region.

Key features of the GPT-4o 2024-08-06 model include:

A Mini API with Vision is a powerful tool for developers. It enables them to build scalable and secure APIs that integrate with various data sources, including images and videos.

With a Mini API, developers can leverage computer vision capabilities to analyze and interpret visual data. This can be particularly useful for applications such as object detection, facial recognition, and image classification.

Worth a look: Azure Openai Batch Api

By integrating computer vision into their APIs, developers can unlock new insights and capabilities that were previously not possible. For example, they can use image analysis to detect anomalies or track changes in images over time.

Developers can also use Mini APIs to integrate with popular machine learning frameworks, making it easier to build and deploy AI-powered applications. This can be especially useful for applications that require real-time processing and analysis of visual data.

By combining the power of APIs with the capabilities of computer vision, developers can create innovative and engaging applications that transform the way people interact with data.

The GPT-4o Realtime API is now available for public preview, and it's a game-changer for real-time conversational interactions.

This API is designed to handle low-latency interactions, making it perfect for applications like customer support agents, voice assistants, and real-time translators.

The GPT-4o audio model is part of the GPT-4o family, which supports "speech in, speech out" conversational interactions.

Consider reading: Azure Openai Api Key

The gpt-4o-realtime-preview model is available for global deployments in two regions: East US 2 and Sweden Central.

This API is ideal for use cases that require live interactions between a user and a model, such as customer support agents and voice assistants.

Azure OpenAI GPT-4o audio is a key component of this API, enabling low-latency conversational interactions.

Take a look at this: Azure Open Ai Api

Fine-tuning models is a powerful feature that allows you to customize our language models to fit your specific needs. This capability is now available in public preview for Azure OpenAI in North Central US and Sweden Central.

You can fine-tune GPT-4o, and new models like gpt-35-turbo-0613, babbage-002, and davinci-002 are also available for fine-tuning. These models replace the legacy ada, babbage, curie, and davinci base models.

Fine-tuning availability is limited to certain regions, so be sure to check the models page for the latest information on model availability in each region. Fine-tuned models have different quota limits than regular models, so keep this in mind when planning your fine-tuning projects.

Here are the new fine-tuning models available in preview:

Additionally, you can now see data ingestion/upload status in the Azure OpenAI Studio, and support for private endpoints & VPNs for blob containers is also available.

DALL-E 3 public preview is now available, offering enhanced image quality and more complex scenes. This latest model from OpenAI also features improved performance when rendering text in images.

One of the standout features of DALL-E 3 is its built-in prompt rewriting, which can enhance images, reduce bias, and increase natural variation. This is a game-changer for anyone looking to create more realistic and diverse images.

If you're interested in trying out DALL-E 3, you can access it through OpenAI Studio or the REST API. Just make sure your OpenAI resource is located in the SwedenCentral Azure region.

Here's a quick rundown of the regions where you can access DALL-E 3:

Note that Azure's deployment capabilities vary by region, so be sure to check the availability of DALL-E 3 in your region before getting started.

Intriguing read: Azure Central Region Outage

The GPT-3.5 Turbo Instruct model is now available on Azure OpenAI Service, offering performance comparable to text-davinci-003. This model can be used with the Completions API.

You can check the models page for the latest information on model availability in each region.

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Asynchronous filtering is now available for all Azure OpenAI customers, which is a game-changer for those working with content filtering.

This feature is designed to improve latency in streaming scenarios, making it perfect for applications that require real-time processing.

With asynchronous filtering, you can run filters in the background, allowing your application to continue running smoothly without interruptions.

Azure OpenAI customers can now enjoy improved performance and efficiency in their content filtering tasks.

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