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Gartner has recognized Microsoft as a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Services, with Microsoft placed furthest in “Completeness of Vision”.

Gartner defines the market as “cloud-hosted or containerized services that enable development teams and business users who are not data science experts to use AI models via APIs, software development kits (SDKs), or applications.”

A square chart split into four quadrants that compares Cloud AI Developer Services on a vertical axis for Ability to Execute and horizontal axis for Completeness of Vision. Microsoft is shown in the top right quadrant as a Leader on both axes.

We are proud to be recognized for our Azure AI Platform. In this post, we’ll dig into the Gartner evaluation, what it means for developers, and provide access to the full reprint of the Gartner Magic Quadrant to learn more.

Scale intelligent apps with production-ready AI

“Although ModelOps practices are maturing, most software engineering teams still need AI capabilities that do not demand advanced machine learning skills. For this reason, cloud AI developer services (CAIDS) are essential tools for software engineering teams.”—Gartner

A staggering 87 percent of AI projects never make it into production.¹ Beyond the complexity of data preprocessing and building AI models, organizations wrestle with scalability, security, governance, and more to make their model’s production ready. That’s why over 85 percent of Fortune 100 companies use Azure AI today, spanning industries and use cases.

More and more, we see developers accelerate time to value by using pre-built and customizable AI models as building blocks for intelligent solutions. Microsoft Research has made significant breakthroughs in AI over the years, being the first to achieve human parity across speech, vision, and language capabilities. Today, we’re pushing the boundaries of language model capabilities with large models like Turing, GPT-3, and Codex (the model powering GitHub Copilot) to help developers be more productive. Azure AI packages these innovations into production-ready general models known as Azure Cognitive Services and use case-specific models, Azure Applied AI Services for developers to integrate via API or an SDK, then continue to fine tune for greater accuracy.

For developers and data scientists looking to build production-ready machine learning models at scale, we support automated machine learning also known as autoML. AutoML in Azure Machine Learning is based on breakthrough Microsoft research focused on automating the time-consuming, iterative tasks of machine learning model development. This frees up data scientists, analysts, and developers to focus on value-add tasks outside operations and accelerate their time to production.

Enable productivity for AI teams across the organization

“As more developers use CAIDS to build machine learning models, the collaboration between developers and data scientists will become increasingly important.”—Gartner

As AI becomes more mainstream across organizations, it’s essential that employees have the tools they need to collaborate, build, manage, and deploy AI solutions effectively and responsibly. As Microsoft Chairman and CEO Satya Nadella shared at Microsoft Build, Microsoft is “building models as platforms in Azure” so that developers with different skills can take advantage of breakthrough AI research and embed them into their own applications. This ranges from professional developers building intelligent apps with APIs and SDKs to citizen developers using pre-built models via Microsoft Power Platform.

Azure AI empowers developers to build apps in their preferred language and deploy in the cloud, on-premises, or at the edge using containers. Recently we also announced the capability to use any Kubernetes cluster and extend machine learning to run close to where your data lives. These resources can be run through a single pane with the management, consistency, and reliability provided by Azure Arc.

Operationalize Responsible AI practices

“Vendors and customers alike are seeking more than just performance and accuracy from machine learning model. When selecting AutoML services, they should prioritize vendors that excel at providing explainable, transparent models with built-in bias detection and compensatory mechanisms.”—Gartner

At Microsoft, we apply our Responsible AI Standard to our product strategy and development lifecycle, and we’ve made it a priority to help customers do the same. We also provide tools and resources to help customers understand, protect, and control their AI solutions, including a Responsible AI Dashboard, bot development guidelines, and built-in tools to help them explain model behavior, test for fairness, and more. Providing a consistent toolset to your data science team not only supports responsible AI implementation but also helps provide greater transparency and enables more consistent, efficient model deployments.

Microsoft is proud to be recognized as a Leader in Cloud AI Developer Services, and we are excited by innovations happening at Microsoft and across the industry that empower developers to tackle real-world challenges with AI. You can read and learn from the complete Gartner Magic Quadrant now.

Learn more


¹Why do 87 percent of data science projects never make it into production? Venture Beat.

Gartner Inc.: “Magic Quadrant for Cloud AI Developer Services,” Van Baker, Svetlana Sicular, Erick Brethenoux, Arun Batchu, Mike Fang, May 23, 2022.

Gartner and Magic Quadrant are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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