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Among the many drivers of the tech ecosystem’s rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of “advanced analysis and logic-based techniques” to simulate human intelligence, AI has numerous use cases for individuals and enterprises across industries. User organizations and their tech partners often develop, customize and run AI implementations on any of a number of popular, cloud-based, AI platforms.

What is an artificial intelligence (AI) cloud platform?

An AI cloud platform provides the tools and interfaces for data scientists, IT professionals and, increasingly, non-technical business staff to create AI-based applications. 

Such platforms must be available in the cloud, but some are supported in on-premises and hybrid configurations as well. AI cloud platforms must provide automated machine learning (autoML), natural language and vision processing functionality. 

These systems are vitally important given the global growth in AI software use. Gartner places the market at an estimated $62.5 billion in 2022 — a 21.3% increase on its value in 2021. By 2025, IDC projects this market to reach $549.9 billion. 


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5 critical functions of an AI cloud platform

In practicality, cloud-based AI development platforms must provide these key functions:

  1. AutoML, vision and natural language functionality. The subset services supported within each of these core functions will largely determine the level and types of applications that can be developed on a platform.
  2. The ability to prototype and test applications quickly and cost effectively.
  3. The capacity to scale from prototype to large-scale data and processing environments easily and cost effectively.
  4. Ready integration into the operational and security environment of other applications currently supported in the enterprise.
  5. Appropriate tools and interfaces to enable IT professionals, as opposed to only data scientists, to develop applications. Cloud AI platforms are increasingly being extended to enable non-technical business analysts and other end users to develop and customize applications as well.

Also read: How machine learning frees up creativity and strategy for marketers

Top 10 artificial intelligence (AI) cloud platforms in 2022

AI platforms have been widely incorporated into the mainstream offerings of the major cloud vendors in the US and China. This has helped to “democratize” AI in that it has increasingly been made practical for not only data scientists but also IT developers – or even the non-technically trained altogether – to develop and deploy more uses of the technology across their organizations. 

As well, vertical (industry-specific) and horizontal (generalized business function) market libraries of modules make it practical for many more enterprises to implement more and more AI-powered applications. 

While it is mostly the major cloud infrastructure providers that have broadened the market by adding AI cloud platforms to their offerings, more specialized vendors have also carved a niche in this space. 

Our list of the “top 10” AI cloud platforms for 2022 includes 

  • Five of these leading US-based cloud service providers: AWS, Microsoft, Google, IBM and Oracle. 
  • Two of their Chinese counterparts: Alibaba and Tencent.
  • One niche AI cloud platform vendor from China: Baidu.
  • Two of their American counterparts: H20.ai and Dataiku.

We have included major products that fared well in market and customer reviews from credible sources online, as well as in the judgment of our own inhouse expertise. 

Among the online sources we tapped are Magic Quadrant and peer insights publications from Gartner and customer reviews from G2

  1. AWS AI Services

AWS has leveraged its dominant position in cloud services to offer a broad range of AI services. Its offerings include machine learning for data scientists and  developers, as well as ML-based business prediction tools for non-technical analysts within an organization. 

Special use cases for large-scale healthcare data and industrial equipment management are also available. 

AWS gets high marks for its language capabilities, including translation services, and for its consistency of interface and APIs with its other cloud services. The image and OCR services, as well as scale limits for some of its packaged business tools, have tempered some of its user scores for execution.

  1. Microsoft Azure AI

Microsoft’s Azure AI services are designed for data scientists developers and, in limited cases, some specifically trained business users. 

The company gets particularly good scores for language services, including its speech recognition, translation, document and OCR, and chat bot functions. 

Users also appreciate its ML capabilities, but some customers find its pricing levels to be challenging.

3. Google Cloud AI

Google has long invested in AI and offers broad autoML, deep learning, text and language, and vision capabilities. Its target users are data scientists and developers. 

The company unified its UI and APIs under the Vertex AI banner and launched the TensorFlow, Apache-based, machine learning and AI framework for deep neural networks in 2015. Google’s Alphabet parent company continues to invest heavily in AI R&D, both internally and via acquisition. 

Users of Google Cloud AI like its integration with other Google services, such as BigQuery, and its ease of testing to scale new models and applications. Some customers praise its natural language and vision capabilities. Some, however, would like to see better documentation and customer support, as well as greater ease of use.

4. IBM Watson Studio

The IBM Watson Studio provides a range of AI and ML services. It runs on the IBM Cloud Pak for Data, which is a cloud-native AI and data platform, as well as in SaaS and on-premises configurations. 

As part of the company’s strategic emphasis on hybrid cloud and AI capabilities, IBM supports higher level consulting and integration services, traditional data science and developer users, and, with Watson Orchestrate, non-technical business users.  

Users praise Watson’s drag and drop interface, which is sufficiently intuitive for non-technical business analysts and helps the product to scale from quick prototypes to the large-scale processing of unstructured data use cases. Lower satisfaction is reported for its real-time data integration and pre-built model capabilities.

5. Oracle Cloud Infrastructure (OCI) AI Services

Oracle is a mature enterprise application and database company that is also now a major player in the cloud infrastructure sector. The company has been labeled a “visionary” by  Gartner for its offerings and roadmap in AI services for 2022. 

Oracle Cloud Infrastructure (OCI) AI Services includes pre-built chat bots, language, speech, vision, prediction and forecasting tools among its offerings. Its horizontal-market, pre-built ML tools are geared for data scientists and developers, with an emphasis on enabling model and data set reuse across the enterprise. 

Its digital assistant function has been well received, with a current ease of development that users would like to see taken further still.

8. Alibaba Cloud

While the Alibaba Cloud subsidiary of Chinese e-commerce giant Alibaba primarily operates in China, the company also has a limited presence across Asia, EMEA and North America. It serves both professional developers and, with its visual interface, business users.  

Alibaba Cloud provides a complete suite of autoML, language and vision services with document and deep learning capability. Along with its ML Learning AI Platform, Alibaba Cloud offers machine translation for NLP, vision search, and a speech recognition and generation platform. Not surprisingly, it offers services optimized for e-commerce applications. 

Users vouch for its image search, but would like to see better support for its AI services generally.

9. Tencent Cloud TI Platform

Chinese tech behemoth Tencent (provider as well of WeChat messaging, gaming, online advertising, streaming entertainment, etc.) offers an AI platform among its many cloud services. Although Tencent has a research facility in the US, it still primarily generates sales in China, where it also has a rich partner ecosystem.

The company is investing heavily in its vision and ML technology in particular, but already has a range of speech, video and sentiment analysis, and computer vision and facial recognition capabilities. 

Tencent is praised especially for its sentiment analysis, but some users consider its full AI suite, including its ML capabilities, to still be maturing more generally.

10. Baidu AI Cloud

Baidu is a niche Chinese company with an AI cloud focus. The company was founded as a search engine provider and has since added video content and entertainment services, but 70% of its business is now generated from its Baidu Core segment, which offers tech services including the Baidu AI Cloud. 

Baidu supports general AI PaaS capabilities, as well as AI PaaS- and SaaS solutions in a limited number of vertical sectors, including smart transportation, finance, manufacturing, utilities, telecom and media. 

Baidu Brain is its core AI engine, and it operates with a wide range of AI and ML capabilities, including deep learning, natural language processing with speech and text, video and structured data analysis, and knowledge graphing. The Baidu AI Cloud is geared toward developers and sometimes leveraged with its deeper AI solutions such as its smart car initiative.

IDC predicts that AI platforms and AI application development and deployment will continue to be the fastest-growing sectors of the AI market. This list provides a starting point for organizations to evaluate the approaches and solutions that best fit their needs.

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