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

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

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

"The technical support from AWS is excellent."

"They offer insights into everyone making calls in my organization."

"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

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

"We've had no problems with SageMaker's stability."

More Amazon SageMaker pros

"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."

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

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

"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."

"The initial setup is very straightforward."

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

"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."

More Google Cloud AI Platform pros

Cons

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

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

"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

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

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."

More Amazon SageMaker cons

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

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

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

"The technical support from Google is not very fast. I think it is about a five out of ten even though they have courses online where I can learn a lot, if I really need support, I have to wait a very long time."

"Improvements in text extraction accuracy and pricing adjustments would be helpful."

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

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."

"I would rate the solution's price a ten out of ten since it is very high."

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

"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 product is expensive."

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

"The tool's pricing is reasonable."

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

More Amazon SageMaker pricing and cost advice

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

"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 pricing is on the expensive side."

"The price of the solution is competitive."

"The licenses are cheap."

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845,040 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

19%

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 10% 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 28% 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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