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- Azure Machine Learning Studio
- Google Cloud AutoML
- Azure Machine Learning Studio-vs-Google Cloud AutoML
Compare Azure Machine Learning and Google Cloud AutoML 
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (36.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure Machine Learning
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (38.8% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningGoogle Cloud AutoML
Star Rating
(25)4.2 out of 5
Market Segments
Small-Business (36.0% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Google Cloud AutoMLSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in Ease of Setup with a score of 8.4, while Google Cloud AutoML falls behind at 7.3. Reviewers mention that Azure's streamlined onboarding process makes it easier for teams to get started quickly.
- Reviewers mention that Azure Machine Learning offers superior Quality of Support with a score of 8.6 compared to Google Cloud AutoML's 7.6. Users on G2 appreciate the responsive customer service and extensive documentation provided by Azure.
- Users say that Azure Machine Learning shines in Scalability with a score of 8.9, while Google Cloud AutoML scores 9.2. However, reviewers mention that Google Cloud's infrastructure allows for more flexible resource allocation, making it a strong contender for large-scale projects.
- G2 users report that Azure Machine Learning's Model Development features, particularly its Pre-Built Algorithms scoring 8.3, are robust, but Google Cloud AutoML's 8.2 score indicates it also provides valuable tools for model training and feature engineering.
- Users on G2 highlight that Azure Machine Learning's Data Ingestion & Wrangling capabilities score 8.7, making it easier to prepare data for analysis. In contrast, Google Cloud AutoML's features in this area are less emphasized, leading to some user frustration.
- Reviewers mention that Azure Machine Learning's Deployment options, particularly its Managed Service scoring 8.8, are user-friendly, while Google Cloud AutoML's deployment features, although effective, are perceived as slightly less intuitive.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableGoogle Cloud AutoML
No pricing availableFree Trial
Azure Machine Learning
No trial information availableGoogle Cloud AutoML
No trial information availableRatings
Meets Requirements
8.5
81
8.8
16
Ease of Use
8.5
80
8.6
16
Ease of Setup
8.3
57
7.8
13
Ease of Admin
8.3
49
8.2
14
Quality of Support
8.6
74
7.7
16
Has the product been a good partner in doing business?
8.6
47
8.6
13
Product Direction (% positive)
9.0
80
8.3
14
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
8.6
22
Not enough data
8.9
21
Not enough data
8.7
22
Not enough data
Model Development
8.6
51
Not enough data
8.9
54
Not enough data
8.3
53
Not enough data
8.7
52
Not enough data
Model Development
8.1
21
Not enough data
8.7
21
Not enough data
8.4
21
Not enough data
Machine/Deep Learning Services
8.1
45
Not enough data
7.9
45
Not enough data
7.8
38
Not enough data
8.2
42
Not enough data
Machine/Deep Learning Services
8.3
21
Not enough data
8.7
21
Not enough data
8.6
20
Not enough data
8.5
21
Not enough data
Deployment
8.8
50
Not enough data
8.7
51
Not enough data
8.9
51
Not enough data
Deployment
8.9
21
Not enough data
8.8
21
Not enough data
9.1
21
Not enough data
Generative AI
8.5
10
Not enough data
8.2
10
Not enough data
7.5
10
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
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Categories
Categories
Shared Categories
Azure Machine Learning and Google Cloud AutoML are categorized as Data Science and Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), and Generative AI Infrastructure
Google Cloud AutoML has no unique categories
Reviews
Reviewers' Company Size
Azure Machine Learning
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
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
Information Technology and Services
16.0%
Research
12.0%
Consulting
4.0%
Retail
4.0%
Program Development
4.0%
Other
60.0%
Most Helpful Reviews
Azure Machine Learning
Most Helpful Favorable Review
Verified User in Civic & Social Organization
As my title says, the process when you start to work with the solution is not painful and is easy to start your implementation
Most Helpful Critical Review
Isabel O.
Verified User in Research
I dislike that the interface is not very user friendly. It took me a while to figure out how to connect the arrows and how the work flow works. I would like it to be more intuitive.
Google Cloud AutoML
Most Helpful Favorable Review
Roberto H.
Verified User in Program Development
This helped our company improve decision-making more quickly and easily. Faced with a set of data of interest, AutoML extracts the dataset of interest from users and determines a high-performance network architecture. It is the quick and easy way to start...
Most Helpful Critical Review
Verified User in Consumer Goods
There’s nothing to dislike but maybe slow
Verified User in Civic & Social Organization
As my title says, the process when you start to work with the solution is not painful and is easy to start your implementation
Roberto H.
Verified User in Program Development
This helped our company improve decision-making more quickly and easily. Faced with a set of data of interest, AutoML extracts the dataset of interest from users and determines a high-performance network architecture. It is the quick and easy way to start...
Isabel O.
Verified User in Research
I dislike that the interface is not very user friendly. It took me a while to figure out how to connect the arrows and how the work flow works. I would like it to be more intuitive.
Verified User in Consumer Goods
There’s nothing to dislike but maybe slow
Discussions
What is Azure Machine Learning Studio used for?
1 comment
AR
In short, to build, deploy, and manage high-quality models faster and with confidence.Read more
Azure Machine Learning has no more discussions with answers
Google Cloud AutoML has no discussions with answers
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