Compare Google Vertex AI vs. Amazon SageMaker vs. Azure ML

What is an ML platform?

An ML platform is a unified set of tools, services and infrastructure designed to facilitate model design, development, training, deployment and management.

ML platforms and ML pipelines are similar in that both include all core capabilities needed to work with models. However, whereas ML pipelines are typically collections of independent ML tools or services that must be manually integrated, ML platforms simplify setup by providing these services in a fully integrated, ready-to-use package.

In addition, ML platforms provide built-in infrastructure for hosting models throughout the development and deployment lifecycle. An ML pipeline doesn't necessarily include infrastructure; it consists only of software tools.

Benefits of ML platforms

Developers don't strictly need an ML platform to build, deploy or operate a model. It's also possible to create an ML pipeline by assembling the tools and infrastructure necessary for model development, training and operation.

But platforms offer several advantages over traditional ML pipelines:

• Minimal setup. ML platforms are turnkey services that require minimal configuration before teams can develop and deploy models.
• Simplified integration. The various features and services within ML platforms are preintegrated, reducing manual effort.
• Built-in infrastructure. Users don't need to purchase, set up or manage computing resources independently. Like PaaS providers in traditional software development, ML platforms provide tooling and infrastructure in one unified package.
• Integrated security. Most ML platforms provide built-in security features, such as access controls to restrict which data users can interact with.
• Prebuilt offerings. While developers can use ML platforms to develop entirely new models, most platforms also provide access to prebuilt offerings such as pretrained models and project templates to automate setup.

How to choose an ML platform

Although all ML platforms provide similar core capabilities surrounding model development and deployment, they vary in important ways.

When selecting a platform, consider the following factors:

• Data compatibility. Which data types will you work with during model development, training and inference? Can the platform efficiently store, clean and manage those data types?
• Security requirements. How rigorous do you need the platform's security controls to be? Does it meet applicable compliance standards, such as certain types of access controls or required data isolation features?
• Integration capabilities. How well does the platform connect with your existing business and IT systems, such as cloud storage for training data and monitoring software for tracking pipeline health?
• Scalability. How well can the platform scale? Does it impose hard limits on amount of training data or number of inference inputs? Are there extra costs for large-scale operations?
• Setup complexity. Do the platform's default configurations align well with your needs without extensive customization? Most ML platforms require minimal setup compared with building an ML pipeline from scratch, but some need more configuration than others.
• Pricing model. How much does the platform charge, and what are those charges based on -- data processing, compute time or other factors?

Comparing SageMaker vs. Azure ML vs. Vertex AI

Now that we've covered how ML platforms work and how to compare them, let's see how the ML platforms from each of the big three public clouds stack up.

Amazon SageMaker

Launched in 2017, SageMaker is AWS' main service for AI and ML development, training and deployment. AWS also offers the Bedrock service for development based on foundation models, but SageMaker is geared more toward developing new models -- although SageMaker does support working with foundation models as well.

Compared with other ML platforms, SageMaker's greatest advantage is its ease of use. This is largely due to its abstraction of the underlying infrastructure during model development and deployment.

SageMaker's serverless inference feature enables users to deploy models without having to manage infrastructure. But the tradeoff associated with this high level of abstraction is limited control over infrastructure compared with other platforms.

From a cost perspective, SageMaker charges primarily based on compute usage and the volume of data processed, although other factors can affect the final bill. AWS offers savings plans for SageMaker that the vendor says can reduce overall costs by up to 64%.

The bottom line

Consider SageMaker for the following needs:

• Your project involves developing a custom model, rather than tweaking an existing foundation model.
• Ease of use is a greater priority than fine-grained infrastructure control.

Azure Machine Learning

Azure ML is the Microsoft Azure cloud service designed for developing and running AI models. It debuted as a preview in 2014 and became generally available in 2018.

Standout features include a drag-and-drop UI that caters to less experienced data scientists. While other cloud-based ML platforms, including SageMaker, now offer drag-and-drop interface features, Azure ML made these a key capability early on.

Azure ML's project templates help automate the process of provisioning ML projects -- although templates are likewise not a feature unique to Azure ML. And while Azure ML's user-friendly approach speeds up development, it might not offer the same depth of customization as other platforms.

Azure ML's pricing model is similar to SageMaker's in that compute resources account for the bulk of costs. Discounted pricing plans are available.

The bottom line

Consider Azure ML for the following needs:

• Your teams want a particularly user-friendly set of ML development and deployment capabilities.
• Development speed is a priority, and you plan to use features like templates to help experienced data scientists accelerate the ML lifecycle.

Google Vertex AI

Released in 2021, Google Vertex AI is the newest and, arguably, the most feature-rich ML platform in this comparison.

Vertex AI offers advanced ML tools and customization options, including a wide range of foundation models and prebuilt extensions to facilitate connection with enterprise APIs, Google Cloud services and more. The tradeoff is that its learning curve can be steeper; users with limited experience in data science or ML development might find the platform more challenging to use.

Vertex AI also has a complex pricing model, with charges varying widely based on the specific features or services users deploy. This can make it more challenging to predict overall Vertex AI costs. However, the upside is that users can potentially save money by strategically choosing which services they run and for how long.

The bottom line

Consider Vertex AI for the following needs:

• You need a very powerful ML platform with advanced capabilities and customization options.
• Your organization has experienced ML teams that can handle Vertex AI's lack of user-friendliness compared with alternatives.

Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.