Azure OpenAI vs ChatGPT: Key Differences and Similarities

Both Azure OpenAI and ChatGPT are powerful AI tools, but they have distinct differences in their architecture and capabilities.

Azure OpenAI is built on top of the Azure cloud platform, allowing for seamless integration with other Azure services.

ChatGPT, on the other hand, is a standalone model that can be integrated with various platforms, but it's not specifically designed for the Azure ecosystem.

Azure OpenAI offers more advanced features, such as the ability to fine-tune models for specific use cases, whereas ChatGPT is more geared towards general-purpose conversations.

The key differences between Azure OpenAI and OpenAI are pretty interesting. Azure OpenAI is available through a training course, so if you're new to it, that's a great place to start.

You can check out the Introduction to Azure OpenAI training course for a solid foundation. This will give you a good understanding of how Azure OpenAI works.

Azure OpenAI has different support options compared to OpenAI, which can be found on the Azure Cognitive Services page.

OpenAI, on the other hand, has a FAQs page that explains how data is stored, which is a big concern for many users.

For those who want to know more about the Service Level Agreements (SLA) for Azure OpenAI, the Cognitive Services section of the SLA page is a great resource.

Azure OpenAI's operational systems can be checked on the OpenAI Status page, which is a good idea if you're planning to use it for critical applications.

Azure OpenAI has regional availability, which can be explored on the Azure OpenAI Service page.

OpenAI models are supported in various countries and territories, which can be found on the OpenAI FAQs page.

Azure OpenAI offers private instances of OpenAI LLMs, allowing you to leverage generative AI on your proprietary data.

This service provides private access through the integration of Private Link, enabling you to access your models within your virtual network infrastructure using private IP addresses.

Private Link ensures the security and compliance of your data, giving you control over who can access your models.

Azure OpenAI Service is hosted on Microsoft Azure, providing a secure environment for your custom AI apps.

By using Azure OpenAI, you can integrate generative AI into your custom applications while keeping your data safe and secure.

ChatGPT Enterprise is a powerful tool that provides access to ChatGPT-4 with no usage caps.

It's up to 2x faster than other versions and comes with API credits to help businesses build their own solutions. This means companies can train the model on their own data, customize it, and optimize it for their industry and desired use cases.

OpenAI delivered ChatGPT Enterprise after less than one year of development, and it's already planning to release another version for smaller organizations called ChatGPT Business.

The tool prioritizes privacy and security, encrypting data in transit and at rest, and only human access is allowed for troubleshooting and retrieving end-user conversations with explicit permission.

ChatGPT Enterprise has been audited for SOC2 Type I compliance and is currently pursuing a SOC2 Type II certification as well.

Businesses can further support compliance with privacy laws by entering into a Data Processing Addendum, and the tool also supports single sign-on, domain verification, and an admin console to manage members.

The business data is retained for purposes of enabling chat history, and businesses have full control over how long it's retained.

Azure OpenAI offers a pay-as-you-go pricing model, which means you only pay for what you use. There are no commitments or contracts to worry about.

The pricing varies depending on the region you're in, and there are additional charges for customization.

Pay-As-You-Go charges vary for different model types and context. Text generation models charge per prompt tokens and completion tokens, while embeddings models and base models charge per usage tokens.

Here's a breakdown of the pricing for different models:

Model customization charges are based on training time and hosting time, with slightly different pricing per region. Here's a breakdown of the pricing for different models:

For image generation, the pricing varies depending on the model and resolution. Here's a breakdown of the pricing for different models:

For speech generation, the pricing is $0.36 per hour for the Whisper model, and $15 per 1M characters for the TTS model.

Generative AI Models are the heart of Azure OpenAI, enabling you to create custom AI apps with secure data. Azure OpenAI Service, hosted on Microsoft Azure, offers private instances of OpenAI LLMs.

You can fine-tune your private GPT models using your own data, customizing them according to your specific needs. This is made possible by the integration of Azure Resource Manager (ARM), providing benefits such as high availability, security, and organizational governance.

Azure OpenAI offers a range of generative AI models, including Image Generation, Large Language Model (LLM), and Transcription models. The types of models available are:

The OpenAI Supported Models include Embeddings, DALL-E, GPT Base, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, GPT-4 Turbo with Vision, GPT-4o, and Whisper. Each model has its own functionalities and maximum request limits.

Model Customization is a key feature of Azure OpenAI Service, allowing you to fine-tune your private GPT models using your own data. This empowers you to customize the models according to your specific needs.

You're charged for model customization based on the number of processed tokens and model storage. This means you only pay for what you use, which can be a cost-effective option for many users.

The cost of model customization varies depending on the type of model and the level of customization. For example, the Command model costs $0.004 to train 1000 tokens, while the Command Light model costs just $0.001.

Here's a breakdown of the costs for each model:

Keep in mind that inference on more than one model unit is only available for Provisioned Throughput, which can be a good option if you need to process large amounts of data.

The world of generative AI models is vast and exciting, with new capabilities emerging every day. One of the key aspects of this field is the variety of models available, each with its unique strengths and limitations.

OpenAI supports a range of models, from embeddings to image generation, and transcription. Here are some of the most notable ones:

The Embeddings model family is great for tasks like anomaly detection, classification, and clustering. It can handle up to 8k tokens.

DALL-E is a powerful image generation model that can generate, edit, or update images from text, with a character limit of 1k-4k.

GPT Base models, such as Babbage-002 and Davinci-002, are designed for generating and understanding text or code. They can handle up to 16k tokens.

For more complex tasks, models like GPT-3.5 Turbo and GPT-4 are available, offering advanced reasoning and chat capabilities, code understanding, and traditional completions tasks. They can handle 4k and 8k tokens, respectively.

If you need even more power, GPT-4 Turbo and GPT-4 Turbo with Vision are the way to go, with capabilities like instruction following and parallel function calling, and image analysis, respectively. They can handle up to 128k tokens.

Lastly, Whisper is a transcription model that can convert audio to text, with a maximum audio size of 25 MB.

Here's a quick summary of the models mentioned:

Azure OpenAI Service offers private instances of OpenAI LLMs, ensuring secure data and custom AI apps.

You can fine-tune your private GPT models using your own data, customizing the models according to your specific needs.

Several categories of models are available via OpenAI and Bedrock, including image generation, large language models, and transcription models.

Image generation models, such as Stable Diffusion and DALL-E, can create images based on textual descriptions or other image inputs.

Large language models (LLMs) are extensively trained on vast text data and understand/produce textual content, powering ChatGPT.

Transcription models can convert audio into text.

Here are some of the specific models supported by Azure OpenAI:

With Azure OpenAI, you can rest assured that your data is secure. Azure OpenAI Service, hosted on Microsoft Azure, offers private instances of OpenAI LLMs.

This means you can leverage generative AI on your proprietary data within your custom applications without worrying about data breaches. Private Link integration allows for private access to your models within your virtual network infrastructure using private IP addresses.

This ensures the security and compliance of your data, giving you peace of mind when working with sensitive information.

Microsoft Services offer a high level of technical expertise required to set up and use Azure OpenAI Services.

To get started, users must create an Azure account and configure their environment, which involves knowledge of cloud computing and APIs.

Azure OpenAI Services provide end-to-end encryption and comply with regulations like GDPR, HIPAA, and ISO 27001 for data privacy and security.

Users have granular control over their data, including who can access it, how long it's stored, and how it's used.

Microsoft retains all prompts and generated content for abuse detection and mitigation for up to 30 days, but customers can apply for an exemption.

The basic pay-per-usage pricing for Azure OpenAI Services is the same as OpenAI's API call costs.

However, there's an additional hourly hosting cost that depends on the base model, ranging from a few cents to a few dollars per hour.

Microsoft provides a Data Protection Addendum, which is required under various data protection laws.

Text Summarization is a crucial task for content creators, and both Azure OpenAI and ChatGPT have made significant strides in this area.

Claude 2.1, a model from Bedrock, is remarkably cost-effective for text summarization, with a 556% cost savings compared to GPT-4 32k.

Processing 300 articles a month, Claude 2.1 can summarize lengthy articles and reports at around 25,000 tokens to about 5,000 tokens for just $96.

GPT-4 32k, on the other hand, would cost a whopping $630 for the same task.

Claude 2.1's distinct ability to summarize text over 32k tokens is a significant advantage, making it a more economical choice for content creators.

In terms of functionality, GPT-4 32k performs slightly better, but Claude 2.1's cost-effectiveness makes it a more attractive option for large-scale text summarization projects.