Amazon SageMaker vs Google Cloud AI Platform comparison

Amazon Web Services (AWS) and Google are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #5 with an average rating of 8.0, while Google is ranked #8 with an average rating of 7.8. Amazon Web Services (AWS) holds a 5.6% mindshare in AIDP, compared to Google’s 4.3% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

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3,302 Comparison Views

91% willing to recommend

Google Cloud AI Platform

Read 9 Google Cloud AI Platform reviews

3,212 Views
2,467 Comparison Views

100% willing to recommend

Amazon SageMaker

Google Cloud AI Platform

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Executive SummaryUpdated on Dec 4, 2024

Amazon SageMaker and Google Cloud AI Platform are competing products in the cloud-based AI and machine learning segment. Google Cloud AI Platform appears to have the upper hand due to its advanced feature set and seamless integration capabilities, making it preferable for feature-rich requirements despite the cost.

Features: Amazon SageMaker offers an end-to-end machine learning solution including easy data labeling, model tuning, and automatic server-less deployment. It provides developers with functionalities like Jupyter Notebooks, built-in algorithms, and seamless AWS integration for a comprehensive machine learning workflow. Google Cloud AI Platform stands out with Vertex AI, known for its sophisticated AutoML and data offerings, facilitating custom model development and real-time prediction. It also supports seamless cross-platform integrations and auto-scaling features.

Room for Improvement: Amazon SageMaker could enhance its flexibility with cross-platform support and improve its advanced feature offerings. While the platform is strong on ease of use, enhancing real-time collaboration capabilities, and user interface design would be beneficial. Google Cloud AI Platform may improve by offering more competitive pricing structures, enhancing customer support response times, and simplifying the deployment process for complex workflows to attract a broader user base.

Ease of Deployment and Customer Service: Google Cloud AI Platform excels in supporting multi-cloud environments with integrated TensorFlow support and streamlined workflows. Concurrently, Amazon SageMaker enhances its usability through intuitive deployment, enriched documentation, and rapid customer support, which favors rapid prototyping and scalability. The key difference lies in Google's broader cross-platform support and Amazon's speed and responsiveness in customer service and deployment.

Pricing and ROI: Amazon SageMaker features an attractive pricing structure with pay-as-you-go options and cost-managed infrastructure, providing savings for frequent training and inference tasks. In contrast, Google Cloud AI Platform incurs higher costs, but its pricing is justified if utilizing advanced features tailored for intricate AI processes, resulting in a potentially better ROI for comprehensive AI needs.

To learn more, read our detailed Amazon SageMaker vs. Google Cloud AI Platform Report (Updated: March 2025).

Buyer's Guide

Amazon SageMaker vs. Google Cloud AI Platform

March 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon SageMaker

Ranking in AI Development Platforms

5th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (3rd)

Google Cloud AI Platform

Ranking in AI Development Platforms

8th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.6%, down from 8.6% compared to the previous year. The mindshare of Google Cloud AI Platform is 4.3%, down from 6.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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Vipul-Kumar

CTO at Intelli AI LLC

An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data

I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"The technical support from AWS is excellent."

"The support is very good with well-trained engineers whose training curriculum is rigorous."

"The deployment is very good, where you only need to press a few buttons."

"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."

"Allows you to create API endpoints."

More Amazon SageMaker pros

"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."

"The initial setup is very straightforward."

"I have seen measurable benefits from Google Cloud AI Platform."

"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."

"The platform's Google Vision API is particularly valuable."

"The solution is able to read 90% of the documents correctly with a 10% error rate."

"The feedback left about these tools was really helpful and informative for us"

"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."

More Google Cloud AI Platform pros

Cons

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."

"SageMaker would be improved with the addition of reporting services."

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."

More Amazon SageMaker cons

"The initial setup was straightforward for me but could be difficult for others."

"The model management on Google Cloud AI Platform could be better."

"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."

"The solution can be improved by simplifying the process to make your own models."

"It could be more clear, and sometimes there are errors that I don't quite understand."

"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."

"Customizations are very difficult, and they take time."

"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."

More Google Cloud AI Platform cons

Pricing and Cost Advice

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

"SageMaker is worth the money for our use case."

"The pricing is comparable."

"The pricing could be better, especially for querying. The per-query model feels expensive."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"The tool's pricing is reasonable."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

"The support costs are 10% of the Amazon fees and it comes by default."

More Amazon SageMaker pricing and cost advice

"For every thousand uses, it is about four and a half euros."

"The pricing is on the expensive side."

"The price of the solution is competitive."

"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."

"The licenses are cheap."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

13%

Computer Software Company

11%

Manufacturing Company

Computer Software Company

16%

Financial Services Firm

11%

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

See all answers

What is your experience regarding pricing and costs for Amazon SageMaker?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

See all answers

What do you like most about Google Cloud AI Platform?

A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up...

See all answers

What is your experience regarding pricing and costs for Google Cloud AI Platform?

For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.

See all answers

What is your primary use case for Google Cloud AI Platform?

I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.

See all answers

Comparisons

Databricks vs Amazon SageMaker

Compared 17% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

More Amazon SageMaker Competitors

IBM Watson Machine Learning vs Google Cloud AI Platform

Compared 29% of the time

Azure OpenAI vs Google Cloud AI Platform

Compared 26% of the time

Hugging Face vs Google Cloud AI Platform

Compared 15% of the time

Microsoft Azure Machine Learning Studio vs Google Cloud AI Platform

Compared 11% of the time

TensorFlow vs Google Cloud AI Platform

Compared 2% of the time

More Google Cloud AI Platform Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

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Buyer's Guide

AI Development Platforms

March 2025

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Also Known As

AWS SageMaker, SageMaker

No data available

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.

Google

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Carousell

Buyer's Guide

Amazon SageMaker vs. Google Cloud AI Platform

March 2025

Free Report: Amazon SageMaker vs. Google Cloud AI Platform

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