Contrasting the Artificial Intelligence Platforms from the US (Amazon AWS AI, Microsoft Azure AI, and Google AI)

Mainstream media often use artificial intelligence and machine learning interchangeably. But they are not the same thing. AI is our pursuit of simulating human thought and decision-making in an automated fashion.

The X-as-a-Service (XaaS) industry is engaged in a conflict that no one is even aware of. The competition to create the most complete, most effective artificial intelligence and machine learning as a service platform (AI/MLaaS) is centered around this. I’ll side-by-side compare the AI and ML systems from AWS, Azure, and GCP in this article.

Let’s take a brief detour. In terms of multi-tenanted architectures, we have had Software as a Service (SaaS) for the past 20+ years (Salesforce and Concur back in 1998–1999) and Infrastructure as a Service (IaaS) for the past 14+ years (AWS launched in 2002 and relaunched in 2006). Of course, since then, services like Desktop as a Service and Database as a Service have become more and more popular. All of which fall under the umbrella term “cloud computing.”

A Thirteen Article Series on Artificial Intelligence (AI) and Machine Learning (ML)

In the 1960s, rule-based computing systems attempted and failed to attain human-level performance on critical tasks. To…

These businesses, along with IBM, Tencent, Baidu, and Alibaba, have been developing sizable machine learning and artificial intelligence as a service platform in addition to their huge IaaS platforms over the past 15 years.
By offering hardware infrastructure and software environments that are already configured for AI and ML use cases, these platforms are aimed, like the majority of development platforms, to decrease the time to value.

I wanted to get an idea of what each of the big three is doing and how they map to each other, so I pulled together a bunch of information from all three vendors as well as this excellent page over at If you are looking at platforms, I hope this will at least give you a starting point.

(Side-by-side simplified heat map of AI & ML Services at AWS, Azure, and GCP)

Machine Learning, Artificial Intelligence, and Robotics Platform

The size and level of development of Amazon’s library immediately stood out to me as I began my review. While Google has a tremendous amount of AI talent, it appears to be lagging behind Microsoft in terms of productization. I believe that the majority of people would be shocked to find that the main three providers already provide this many developer-friendly AI services.

Look at the “Functions” column. It is astounding how many services are already offered.

(Detailed view of Robotics, AI, and ML services from AWS, Azure, and GCP)

Databases & Analytics

In Dr. Kai-Fu Lee’s excellent book AI Superpowers, one of the noteworthy findings is that the US excels in two areas: basic research and possessing a lot of organized and semi-structured data (from corporate ERP systems).

In connection with that, over the past few years, the top three have been developing and offering a number of more effective and cost-competitive DBaaS solutions, and as a result, they are now the repository of enormous amounts of structured and semi-structured client data.

On top of AWS, Azure, and GCP, service providers like Snowflake are developing Datawarehouse-as-a-Service (DWaaS). You may now train AI and ML systems with even more data.

(Detailed view of Database & Analytics offerings that sit underneath the AI/ML platforms.)


Not much to say here except that all three are of course very developer-friendly and provide a lot of tools to help with the entire development process.

(Detailed view of the development tools from each vendor.)

Infrastructure as a service

AI and ML ultimately sit on top of big and fast and cheap compute and storage, which is IaaS in a nutshell.

(Detailed view of the basic IaaS functions at AWS, Azure, and GCP.)

It is clear that this is a moment in time. Additionally, it is quickly developing and evolving. I’m eager to observe how these service stacks change over the next year or two, especially how the AI services change.

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