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Compare Amazon SageMaker and Google Cloud AutoML 
Featured Products
At a Glance
Market Segments
Enterprise (34.9% of reviews)
Small-Business (45.5% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Amazon SageMaker
Star Rating
(48)4.2 out of 5
Market Segments
Enterprise (34.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerGoogle Cloud AutoML
Star Rating
(22)4.1 out of 5
Market Segments
Small-Business (45.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Google Cloud AutoMLAmazon SageMaker vs Google Cloud AutoML
When assessing the two solutions, reviewers found Google Cloud AutoML easier to use. However, Amazon SageMaker is easier to set up and administer. Reviewers also preferred doing business with Amazon SageMaker overall.
- Amazon SageMaker and Google Cloud AutoML both meet the requirements of our reviewers at a comparable rate.
- When comparing quality of ongoing product support, reviewers felt that Amazon SageMaker is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Amazon SageMaker over Google Cloud AutoML.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableGoogle Cloud AutoML
No pricing availableFree Trial
Amazon SageMaker
No trial information availableGoogle Cloud AutoML
No trial information availableRatings
Meets Requirements
8.6
38
8.6
14
Ease of Use
8.4
39
8.6
14
Ease of Setup
8.5
26
7.4
11
Ease of Admin
8.4
20
7.9
12
Quality of Support
8.6
34
7.5
14
Has the product been a good partner in doing business?
9.2
20
8.3
11
Product Direction (% positive)
9.1
37
8.9
11
Features by Category
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.3
20
Not enough data
Model Development
8.7
29
Not enough data
8.2
28
Not enough data
8.3
33
Not enough data
8.9
33
Not enough data
Model Development
8.4
19
Not enough data
Machine/Deep Learning Services
8.9
26
Not enough data
9.1
28
Not enough data
8.9
25
Not enough data
9.0
28
Not enough data
Machine/Deep Learning Services
9.2
17
Not enough data
9.2
18
Not enough data
Deployment
8.7
33
Not enough data
8.7
33
Not enough data
9.0
31
Not enough data
Generative AI
8.6
6
Not enough data
9.2
6
Not enough data
8.3
5
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Amazon SageMaker and Google Cloud AutoML are categorized as Data Science and Machine Learning Platforms and Low-Code Machine Learning Platforms
Unique Categories
Amazon SageMaker is categorized as MLOps Platforms and Generative AI Infrastructure
Google Cloud AutoML has no unique categories
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Google Cloud AutoML
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
20.9%
Computer Software
16.3%
Marketing and Advertising
4.7%
Internet
4.7%
Hospital & Health Care
4.7%
Other
48.8%
Research
13.6%
Information Technology and Services
13.6%
Computer Software
9.1%
Program Development
4.5%
Pharmaceuticals
4.5%
Other
54.5%
Alternatives
Discussions
What is the best way to integrate Sagemaker models with Kubernetes?
1 Comment
VJ
https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-operators-for-kubernetes/Read more
How do i make this platform reach to most of my developers?
1 Comment
VJ
you can manage the access via IAM users and roles and give them access as per their need, Sagemaker by default has all the basic AWS feature and you can...Read more
Amazon SageMaker has no more discussions with answers
Google Cloud AutoML has no discussions with answers
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