Azure AI Pricing and Services Explained

Azure AI pricing can be a bit complex, but I'll break it down for you. Azure offers a pay-as-you-go model, where you only pay for the resources you use.

Microsoft provides a range of AI services, including Cognitive Services, Machine Learning, and Bot Service. These services can be used to build intelligent applications, from chatbots to predictive analytics.

To get started with Azure AI, you'll need to create a free account. This will give you access to a free tier of services, including 5 million transactions per month for Cognitive Services.

Azure AI pricing can be complex, but it's good to know your options. There are three pricing models for Azure OpenAI Service: Standard (On-Demand), Provisioned (PTUs), and Batch API.

The Standard (On-Demand) model is a pay-as-you-go approach for input and output tokens. This means you only pay for what you use. The Provisioned (PTUs) model, on the other hand, allows you to allocate throughput with predictable costs, with monthly and annual reservations available to reduce overall spend.

Azure AI Foundry is monetized through individual products, and each product has its own billing model and price. The platform is free to use and explore, but pricing occurs at deployment level.

To give you a better idea of the pricing models, here are the main deployment options for Azure OpenAI Service:

Keep in mind that the pricing for Azure AI services also varies depending on the type of model you use, such as language models, legacy language models, speech, base model, fine-tuning, and so on.

Azure OpenAI Service pricing is structured into three main models: Standard (On-Demand), Provisioned (PTUs), and Batch API.

The Standard (On-Demand) pricing model allows you to pay as you go for input and output tokens.

Provisioned (PTUs) enables you to allocate throughput with predictable costs, with monthly and annual reservations available to reduce your overall spend.

Batch API offers a 50% discount on Global Standard Pricing for completions within 24 hours for global deployments in three regions.

Azure OpenAI Service can be deployed in three ways: Global Deployment, Data Zone Deployment, and Regional Deployment.

Global Deployment is available as a Global SKU.

Data Zone Deployment is geographic-based, with options for EU or US regions.

Regional Deployment is available in up to 27 local regions.

The Level Agreement is a crucial aspect of using Azure OpenAI Service. Reviewing the Service Level Agreement (SLA) is essential to understand the uptime and availability commitments made by Azure.

The SLA for Azure OpenAI Service is designed to ensure high availability and reliability. It guarantees that the service will be available at least 99.9% of the time, except for planned maintenance and unforeseen circumstances.

To put this into perspective, 99.9% uptime translates to a maximum of 43.8 minutes of downtime per month. This means you can rely on Azure OpenAI Service to be up and running for your applications and workflows most of the time.

However, it's essential to note that the SLA does not cover downtime caused by external factors, such as network connectivity issues or power outages.

The OpenAI Service is a game-changer for developers and companies looking to integrate advanced AI models into their applications. It's provided by Microsoft in collaboration with OpenAI, and it's a powerful tool for text generation and analysis.

The service uses GPT (Generative Pre-trained Transformers) models, which are designed to process and generate text in natural language. These models are built on a Transformer-type neural network architecture, which allows them to manage large text sequences through attention mechanisms.

The attention mechanisms in these models enable them to focus on specific parts of the text, significantly improving contextual understanding and the quality of language generation. This is a huge advantage for developers who want to create more accurate and effective language models.

The Azure OpenAI Service can be deployed in three ways: Global Deployment, Data Zone Deployment, and Regional Deployment. This allows companies to choose the deployment method that best fits their needs.

Here are the three deployment options:

The Azure OpenAI Service offers three pricing plans: Standard (On-Demand), Provisioned (PTUs), and Batch API. Each plan has its own unique features and pricing structure.

Azure AI Services offer a wide range of capabilities to help developers build intelligent applications. Anomaly Detector can detect real-time anomalies in time series data, making it easy to identify spikes, dips, and changes in trend.

Azure AI Vision and Custom Vision allow developers to add intelligent functionality to their applications, including face detection, object recognition, and text extraction from images. You can even train your own models using customized images and labels.

Azure AI Search (also known as Azure Cognitive Search) provides a cloud-based search service that can extract text from scanned images or documents using optical character recognition (OCR). It also uses machine learning algorithms to understand the meaning of the text and provide relevant results.

Azure AI Machine Learning simplifies collaboration between data scientists and developers, allowing them to explore data, develop predictive models, and optimize performance without managing infrastructure.

The Realtime API is a powerful tool that enables fast and efficient processing of data. It's featured in the new Realtime API, which supports audio/speech capabilities.

One of the standout features of the Realtime API is its ability to handle multilingual speech-to-speech functionality. This means that users can interact with the API in their native language, making it a more inclusive and accessible service.

The model GPT-4o-Realtime-Preview is specifically designed to work with the Realtime API, and it's capable of processing audio and speech inputs. This opens up a wide range of possibilities for applications that require real-time speech recognition or synthesis.

GPT-4 Mini is a cost-efficient small model that's perfect for developers who want to get started with AI without breaking the bank. It has vision capabilities and a context of 128K, which is quite impressive.

The GPT-4 Mini model has a knowledge cutoff of October 2023, which means it's up to date with the latest information. This is especially useful for applications that require a good understanding of current events.

One of the best things about GPT-4 Mini is its affordability. Unfortunately, the pricing information is not available in the provided text, so we can't provide a specific figure. However, we can say that it's a great option for developers who want to experiment with AI without overspending.

Here's a quick rundown of the GPT-4 Mini model's features:

Note that the pricing information is not available for these models, so we can't provide a specific figure. But hey, at least we know they're affordable!

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

Developers can use the Assistants API to build AI Assistants that can understand and respond to natural language inputs. The API is flexible and can be used with various language models to create Assistants that suit different needs.

The Assistants API charges fees for tool usage, including File Search and Code Interpreter. The fees are as follows:

Note that GB refers to binary gigabytes, where 1 GB is 2^30 bytes. If your Assistant calls Code Interpreter simultaneously in two different threads, this would create two Code Interpreter sessions (2 * $-).

The Assistants API is designed to be efficient and cost-effective, with fees that are based on actual usage. This means that developers only pay for what they use, making it a great option for building AI Assistants.

By using the Assistants API, developers can create AI Assistants that are intelligent, conversational, and highly customizable. This can help to improve the user experience and create new opportunities for innovation and growth.

Azure AI offers a range of machine learning services that can help you build intelligent applications.

Azure Machine Learning is a platform that simplifies the creation, training, and deployment of large-scale machine learning models. It offers an intuitive interface and automation capabilities that make collaboration between data scientists and developers easier.

The platform allows you to explore data, develop predictive models, and optimize performance without having to manage the underlying infrastructure. Azure integrated services, such as Azure Notebooks and Azure DevOps, complete the ecosystem by offering an integrated workflow for experimenting, testing, and putting models into production quickly and efficiently.

Azure AI Personalizer is an artificial intelligence service that helps applications make smarter decisions at scale using reinforcement learning. It processes information on the state of the application, the context, and the users, and a set of possible decisions and related attributes to determine the best decision to make.

The service is accessible through an SDK client library, REST API, or through the Personalizer web portal. Feedback from the application is sent to Personalizer to learn how to improve its ability to make decisions in near real-time.

You can use Azure AI Vision to add intelligent functionality to your applications using pre-trained machine learning models from Microsoft. The service offers several functionalities, including understanding the movement of people through a space, detecting faces in images, and extracting text from images.

Azure AI Vision also includes the Computer Vision service, which can analyze and understand the contents of images. It can identify objects, people, text, scenes, and activities in images, as well as detect any inappropriate content.

Custom Vision, on the other hand, allows developers to create, distribute, and improve their image classifiers. You can train your model using customized images and labels, and the algorithm will analyze the images and calculate its accuracy by testing itself on the same images.

Azure AI Vision and Custom Vision are two powerful tools that allow developers to easily add intelligent functionality to their applications.

Azure AI Vision is a suite of pre-trained machine learning models from Microsoft, designed to help developers add intelligent functionality to their applications.

Using Azure Vision Studio, a set of UI-based tools, you can explore, build, and integrate functionality from Azure AI Vision without writing any code.

Azure AI Vision offers several functionalities, including understanding the movement of people through a space, detecting faces in images, and extracting text from images.

The Azure Computer Vision service can analyze and understand the contents of images, identifying objects, people, text, scenes, and activities in images, as well as detecting any inappropriate content.

Custom Vision allows developers to create, distribute, and improve their image classifiers and train their model using customized images and labels.

Here are the key features of Azure AI Vision and Custom Vision:

Azure AI Video Indexer is a cloud application that uses Azure Media Services and Azure AI services to extract information from video and audio content, running more than 30 artificial intelligence models.

Azure AI Video Indexer insights can be applied to multiple scenarios, including content creation, advertising optimization, and management of digital assets and media libraries.

Azure Cognitive Search is a cloud-based search service that provides programmers with the infrastructure, APIs, and tools to create advanced search experiences from private and heterogeneous data collections.

It offers two different indexing engines: Microsoft's proprietary Natural Language Processing (NLP) technology and the Apache Lucene open source library analyzers.

The service can use optical character recognition (OCR) to extract text from scanned images or documents without additional intervention.

This allows Cognitive Search to understand the meaning of the text and find information even if a document doesn't contain the exact keyword searched.

Content Moderator is an AI service that automatically analyzes texts, images, and videos to detect potentially offensive or unwanted content. It's a comprehensive solution that can be used in various scenarios, such as online marketplaces, social messaging platforms, and educational solutions.

The service is powered by artificial intelligence and is suitable for detecting harmful user-generated and AI-generated content. It's designed to help applications and services manage potentially unwanted content.

Azure AI Content Moderator is available through a REST API and client library SDK in various development languages, making it easy to integrate into existing systems.