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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 (39.3% of reviews)
Small-Business (40.9% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure Machine Learning
Star Rating
(87)4.3 out of 5
Market Segments
Enterprise (39.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningGoogle Cloud AutoML
Star Rating
(22)4.1 out of 5
Market Segments
Small-Business (40.9% 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, making Azure a more favorable choice for those prioritizing quick deployment.
- 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, indicating that users may find more reliable assistance when using Azure.
- G2 users highlight Azure's Scalability with a score of 8.9, while Google Cloud AutoML shines even brighter with a score of 9.2, suggesting that users looking for robust scaling capabilities may prefer Google Cloud AutoML.
- Users on G2 report that Azure Machine Learning provides a better experience in Model Development with a score of 8.7, while Google Cloud AutoML's score of 8.8 indicates a slight edge in Pre-Built Algorithms at 8.2, showcasing its strengths in ready-to-use solutions.
- Reviewers say that Azure Machine Learning's Data Ingestion & Wrangling capabilities are rated at 8.7, which is higher than Google Cloud AutoML's offerings, suggesting that Azure may be more efficient in handling data preparation tasks.
- Users report that Google Cloud AutoML's Generative AI features, particularly in AI Text Generation with a score of 8.5, are highly regarded, while Azure's offerings in this area are less emphasized, indicating a potential advantage for Google in creative AI applications.
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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
80
8.6
13
Ease of Use
8.6
79
8.6
13
Ease of Setup
8.4
56
7.3
10
Ease of Admin
8.3
49
7.9
12
Quality of Support
8.6
73
7.6
13
Has the product been a good partner in doing business?
8.6
47
8.3
11
Product Direction (% positive)
9.1
79
7.9
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
Feature Not Available
Not enough data
Feature Not Available
Not enough data
System
8.7
21
Not enough data
8.9
20
Not enough data
8.7
21
Not enough data
Model Development
8.6
50
Not enough data
8.9
53
Not enough data
8.3
52
Not enough data
8.7
51
Not enough data
Model Development
8.2
20
Not enough data
8.8
20
Not enough data
8.5
20
Not enough data
Machine/Deep Learning Services
8.1
44
Not enough data
7.9
44
Not enough data
7.7
37
Not enough data
8.2
41
Not enough data
Machine/Deep Learning Services
8.3
20
Not enough data
8.7
20
Not enough data
8.6
19
Not enough data
8.5
20
Not enough data
Deployment
8.8
49
Not enough data
8.7
50
Not enough data
8.9
50
Not enough data
Deployment
8.8
20
Not enough data
8.8
20
Not enough data
9.2
20
Not enough data
Generative AI
Feature Not Available
Feature Not Available
Feature Not Available
Feature Not Available
Feature Not Available
Feature Not Available
Scalability and Performance - Generative AI Infrastructure
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Security and Compliance - Generative AI Infrastructure
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Usability and Support - Generative AI Infrastructure
Feature Not Available
Not enough data
Feature Not Available
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
28.6%
Computer Software
14.3%
Management Consulting
8.3%
Education Management
6.0%
Higher Education
4.8%
Other
38.1%
Information Technology and Services
18.2%
Research
13.6%
Program Development
4.5%
Pharmaceuticals
4.5%
Online Media
4.5%
Other
54.5%
Most Helpful Reviews
Azure Machine Learning
Most Helpful Favorable Review
Sreenivasa Rao R.
Verified User in Information Technology and Services
Most of the services are predefined to meet the business needs.Easy to create the experiment, understanding of the algorithm is easy and we can able to deploy the model as a web service.
Most Helpful Critical Review
Verified User in Farming
Microsoft UI, its more like a microsoft thing than Machine learning stuff.
Google Cloud AutoML
Most Helpful Favorable Review
Verified User in Entertainment
Google’s cloud implementation is top notch!
Most Helpful Critical Review
Verified User in Consumer Goods
There’s nothing to dislike but maybe slow
Sreenivasa Rao R.
Verified User in Information Technology and Services
Most of the services are predefined to meet the business needs.Easy to create the experiment, understanding of the algorithm is easy and we can able to deploy the model as a web service.
Verified User in Entertainment
Google’s cloud implementation is top notch!
Verified User in Farming
Microsoft UI, its more like a microsoft thing than Machine learning stuff.
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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