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 8.0. Amazon Web Services (AWS) holds a 5.3% mindshare in AIDP, compared to Google’s 3.9% 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 37 Amazon SageMaker reviews

3,601 Views
2,485 Comparison Views

91% willing to recommend

Google Cloud AI Platform

Read 9 Google Cloud AI Platform reviews

2,957 Views
2,099 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: June 2025).

Buyer's Guide

Amazon SageMaker vs. Google Cloud AI Platform

June 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 (2nd)

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 June 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.3%, down from 8.1% compared to the previous year. The mindshare of Google Cloud AI Platform is 3.9%, down from 6.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

Read full review

HaiPham

Managing Director at sea-solutión

Helps with text extraction from images and has a straightforward setup process

We use Google Cloud AI Platform to extract text from images, such as forms. The platform's Google Vision API is particularly valuable. It helps with text extraction from images. Improvements in text extraction accuracy and pricing adjustments would be helpful. The solution is stable. We have…

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 are the ability to store artifacts and gather reports and measures from experiments."

"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."

"The product aggregates everything we need to build and deploy machine learning models in one place."

"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."

"The most valuable feature of Amazon SageMaker is SageMaker Studio."

"I have seen a return on investment, probably a factor of four or five."

"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 these models, making accessing them convenient as needed."

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

More Amazon SageMaker pros

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

"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."

"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."

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

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

"The initial setup is very straightforward."

"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

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

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

"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."

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

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

"Lacking in some machine learning pipelines."

"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."

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

More Amazon SageMaker cons

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

"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."

"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."

"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."

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

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

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

More Google Cloud AI Platform cons

Pricing and Cost Advice

"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 tool's pricing is reasonable."

"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."

"Amazon SageMaker is a very expensive product."

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

"The solution is relatively cheaper."

"Databricks solution is less costly than Amazon SageMaker."

More Amazon SageMaker pricing and cost advice

"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."

"The price of the solution is competitive."

"The pricing is on the expensive side."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

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?

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

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 13% 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 8% of the time

DataRobot vs Amazon SageMaker

Compared 8% of the time

More Amazon SageMaker Competitors

IBM Watson Machine Learning vs Google Cloud AI Platform

Compared 31% of the time

Azure OpenAI vs Google Cloud AI Platform

Compared 25% of the time

Hugging Face vs Google Cloud AI Platform

Compared 14% of the time

Microsoft Azure Machine Learning Studio vs Google Cloud AI Platform

Compared 8% of the time

TensorFlow vs Google Cloud AI Platform

Compared 3% of the time

More Google Cloud AI Platform Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

June 2025

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Buyer's Guide

AI Development Platforms

June 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

June 2025

Free Report: Amazon SageMaker vs. Google Cloud AI Platform

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