Azure GPT Service for Developers and Businesses

Azure GPT is a game-changer for developers and businesses alike. It provides a scalable and secure way to deploy AI models, making it easier to integrate AI into their applications.

With Azure GPT, developers can leverage a wide range of pre-trained models, including language, computer vision, and speech recognition models. This allows them to build more sophisticated and accurate AI-powered applications.

Azure GPT also offers a range of tools and services to help developers fine-tune and customize these models to meet their specific needs. This includes support for model training, deployment, and management, making it easier to get started with AI development.

By using Azure GPT, businesses can unlock new revenue streams and improve operational efficiency through AI-powered applications. Whether it's chatbots, virtual assistants, or predictive analytics, Azure GPT makes it possible to build and deploy AI solutions at scale.

To get started with Azure OpenAI Service, you need to create an Azure OpenAI Service resource in your Azure subscription.

You can create a resource via Azure portal, Azure CLI, or Azure PowerShell.

To deploy a model, you'll need an Azure OpenAI Service resource. For example, you can deploy a model like GPT-4o.

Once you have a deployed model, you can try real-time audio and assistants in the playgrounds or via code.

A Limited Access registration form is required to access some Azure OpenAI Service models or features.

To access these features, learn more on the Azure OpenAI Limited Access page.

Azure GPT is a powerful tool that offers a range of features to help users create and manage their own conversational AI models.

It supports multiple languages, including English, Spanish, French, and many more, making it a versatile option for global applications.

With Azure GPT, you can generate text in various tones and styles, from formal to casual, and even create custom tone profiles for specific use cases.

You can fine-tune your models using your own data, allowing for highly personalized and accurate results.

Azure GPT also includes a built-in API for easy integration with other applications and services, making it a seamless addition to your tech stack.

It can process large volumes of text quickly and efficiently, making it ideal for applications that require high throughput.

Azure GPT offers flexible pricing options to suit various needs. You can choose from three pricing models: Standard (On-Demand), Provisioned (PTUs), and Batch API.

The Standard (On-Demand) model lets you pay-as-you-go for input and output tokens, making it a great option for small projects or testing. You can also allocate throughput with predictable costs using Provisioned (PTUs), which is ideal for larger-scale deployments.

With Provisioned (PTUs), you can opt for monthly or annual reservations to reduce your overall spend. This can be a cost-effective choice for long-term projects or high-volume usage.

Azure GPT also offers a Batch API for global deployments, which returns completions within 24 hours and provides a 50% discount on Global Standard Pricing. You can choose from three deployment options: Global Deployment, Data Zone Deployment, and Regional Deployment.

Here are the three deployment options:

You can use the Pricing calculator to estimate your expected monthly costs for using any combination of Azure products.

There are several pricing options to choose from when using the Azure OpenAI Service. You can pay-as-you-go with the Standard (On-Demand) plan, which charges for input and output tokens.

The Provisioned (PTUs) plan allows you to allocate throughput with predictable costs, with options for monthly and annual reservations to reduce your overall spend.

If you're planning a global deployment, you can take advantage of the Batch API, which returns completions within 24 hours for a 50% discount on Global Standard Pricing.

You can deploy the Azure OpenAI Service in three different ways: Global Deployment, Data Zone Deployment, and Regional Deployment. The Global Deployment option uses a Global SKU, while the Data Zone Deployment option is based on your geographic location, with options for the EU or US.

The Regional Deployment option allows you to deploy in a local region, with up to 27 regions to choose from.

Here's a summary of the pricing options:

Let's take a closer look at the Service Level Agreement for Azure OpenAI Service. SLA is reviewed for Azure OpenAI Service. This ensures high uptime and reliability for your applications.

Azure OpenAI Service has a 99.9% monthly uptime guarantee. This means that your applications will be available at least 99.9% of the time.

Downtime is measured as the total time your application is unavailable due to Azure OpenAI Service issues. This excludes planned maintenance and any issues outside of Azure OpenAI Service's control.

Azure OpenAI Service is designed to be highly available and scalable. This means you can count on your applications to be up and running even during high traffic periods.

If Azure OpenAI Service fails to meet the SLA, you may be eligible for credits on your bill. This is a great way to mitigate any losses due to downtime.

Azure OpenAI Service's SLA is subject to change, so be sure to review it regularly. This will ensure you're always up to date on the latest terms and conditions.

GPT-4o is the most advanced multimodal model that’s faster and cheaper than GPT-4 Turbo with stronger vision capabilities.

The model has 128K context and an October 2023 knowledge cutoff, indicating its ability to process and understand vast amounts of information.

The Assistants API is a powerful tool for building AI Assistants in applications, making it easy for developers to integrate AI capabilities. The API uses tokens that are billed at the chosen language model's per token input/output rates used with each Assistant.

Developers can expect to pay fees for tool usage, including $-/GB of vector-storage per day for File Search, with 1 GB free. The Code Interpreter tool is charged at $- per session.

If your assistant calls Code Interpreter simultaneously in two different threads, this would create two Code Interpreter sessions, each active by default for one hour. Inference cost, including input and output, varies based on the GPT model used with each Assistant.

The Realtime API is a powerful tool that enables seamless communication and interaction. It features a model called GPT-4o-Realtime-Preview that supports audio/speech capabilities.

This model includes multilingual speech-to-speech functionality, allowing for smooth communication across different languages.

The Assistants API is a powerful tool for developers to build AI Assistants in their applications. It makes it easy to integrate AI capabilities into various projects.

The Assistants API charges based on the chosen language model's per token input/output rates used with each Assistant. This means that the cost will vary depending on the specific model and usage.

To give you a better idea, here are the fees for tool usage in the Assistants API:

If your assistant calls Code Interpreter simultaneously in two different threads, this would create two Code Interpreter sessions, each with its own fee. Each session is active by default for one hour, which means you would only pay the fee once if your user keeps giving instructions to Code Interpreter in the same thread for up to one hour.

Inference cost, which includes input and output, varies based on the GPT model used with each Assistant.

To successfully make a call against Azure OpenAI, you'll need an endpoint and a key. Copy your endpoint and access key from the Azure portal's Resource Management section.

The endpoint and access key can be used to authenticate API calls. You can use either KEY1 or KEY2, allowing you to securely rotate and regenerate keys without causing a service disruption.

The format of your first line of the command with an example endpoint would appear as follows: curl https://docs-test-001.openai.azure.com/openai/deployments/{YOUR-DEPLOYMENT_NAME_HERE}/chat/completions?api-version=2024-02-01.

To edit your Spring application, you need to start by editing the pom.xml file. This file is located in the root of your project directory and contains the project's configuration information. Simply open it in your preferred editor or IDE and replace the existing content with the provided code.

The pom.xml file is where you define your project's dependencies, including the Spring Boot Starter and the Azure OpenAI Spring Boot Starter. You'll also need to specify the version of Java you're using, which in this case is Java 17.

To make changes to the application, you'll also need to update the AiChatApplication.java file. This file contains the main class of your application and is responsible for setting up the Spring Boot environment.

To run the application, navigate back to the project root folder and execute the following command: ./mvnw spring-boot:run. This will start the application and make it available for testing.

Note: For production, it's recommended to use a secure way of storing and accessing your credentials, such as Azure Key Vault.