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Compare Azure Machine Learning and Vertex AI 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
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 LearningVertex AI
Star Rating
(593)4.3 out of 5
Market Segments
Small-Business (41.0% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
Free Trial is available
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Vertex AI excels in its AI Data Encryption capabilities, achieving a score of 9.2, which reviewers mention provides robust security for sensitive data. In contrast, Azure Machine Learning, while still strong, scores slightly lower at 8.2 in this area.
- Reviewers mention that Azure Machine Learning offers superior Natural Language Processing features, scoring 7.9 compared to Vertex AI's 8.6. Users appreciate the nuanced understanding and generation capabilities that Azure provides, making it a preferred choice for text-heavy applications.
- G2 users highlight Vertex AI's AI Model Training Scalability with a score of 8.5, which reviewers say allows for efficient handling of large datasets. Azure Machine Learning, however, scores higher at 8.7, indicating a more robust infrastructure for scaling model training.
- Users on G2 report that Azure Machine Learning shines in Ease of Setup, scoring 8.4, which reviewers mention makes it more accessible for teams with limited technical expertise. Vertex AI, while still user-friendly, scores slightly lower at 8.2.
- Reviewers mention that Vertex AI's AI High Availability feature scores an impressive 9.2, indicating a strong commitment to uptime and reliability. Azure Machine Learning, while competitive, scores lower in this area, which users say could impact mission-critical applications.
- Users say that Azure Machine Learning's Model Monitoring capabilities, scoring 8.5, provide comprehensive insights into model performance, which reviewers appreciate for maintaining model accuracy over time. Vertex AI, with a score of 8.6, is also strong but lacks some of the advanced features found in Azure.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableVertex AI
Try Vertex AI Free
Pay As You Go
Per Month
New customers get $300 in free credits to spend on Vertex AI.
- A unified UI for the entire ML workflow
- Pre-trained APIs for vision, video, natural language, and more
- End-to-end integration for data and AI
Free Trial
Azure Machine Learning
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
8.5
81
8.6
359
Ease of Use
8.5
80
8.2
368
Ease of Setup
8.3
57
8.1
291
Ease of Admin
8.3
49
7.9
142
Quality of Support
8.6
74
8.1
335
Has the product been a good partner in doing business?
8.6
47
8.2
136
Product Direction (% positive)
9.0
80
9.2
353
Features by Category
Deployment
Not enough data
8.3
73
Not enough data
8.1
74
Not enough data
8.3
74
Not enough data
8.3
70
Not enough data
8.8
70
Deployment
Not enough data
8.4
73
Not enough data
8.3
72
Not enough data
8.4
71
Not enough data
8.5
71
Not enough data
8.7
69
Management
Not enough data
8.3
70
Not enough data
8.5
69
Not enough data
8.0
69
Not enough data
8.1
69
Operations
Not enough data
8.2
69
Not enough data
8.4
70
Not enough data
8.3
70
Management
Not enough data
8.1
68
Not enough data
8.4
69
Not enough data
8.3
68
Generative AI
Not enough data
8.2
34
Not enough data
8.4
34
System
8.6
22
8.2
170
Model Development
8.6
51
8.5
202
8.9
54
7.9
179
8.3
53
8.4
200
8.7
52
8.5
202
Model Development
8.4
21
8.3
165
Machine/Deep Learning Services
8.1
45
8.2
200
7.9
45
8.4
196
7.8
38
8.2
195
8.2
42
8.2
178
Machine/Deep Learning Services
8.7
21
8.5
165
8.5
21
8.5
163
Deployment
8.8
50
8.2
193
8.7
51
8.3
194
8.9
51
8.5
193
Generative AI
8.5
10
8.3
102
8.2
10
8.3
102
7.5
10
8.1
103
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
34
Not enough data
7.9
34
Not enough data
7.7
34
Not enough data
7.9
34
Not enough data
8.5
34
Not enough data
7.8
34
Not enough data
8.0
34
Scalability and Performance - Generative AI Infrastructure
Not enough data
8.9
28
Not enough data
8.6
28
Not enough data
8.5
28
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.2
28
Not enough data
7.8
28
Not enough data
7.9
28
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.4
28
Not enough data
8.1
28
Not enough data
8.3
28
Security and Compliance - Generative AI Infrastructure
Not enough data
8.6
28
Not enough data
8.5
28
Not enough data
8.9
28
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
28
Not enough data
8.3
28
Integration - Machine Learning
Not enough data
8.5
67
Learning - Machine Learning
Not enough data
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
9.0
23
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
23
Not enough data
9.0
23
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
23
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.3
23
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
23
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
22
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
23
Not enough data
8.7
22
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
22
Not enough data
8.9
22
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Not enough data
9.1
22
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
23
Not enough data
9.0
23
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
23
Customization - AI Agent Builders
Not enough data
8.5
27
Not enough data
7.6
27
Not enough data
8.3
26
Functionality - AI Agent Builders
Not enough data
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Data and Analytics - AI Agent Builders
Not enough data
7.7
25
Not enough data
7.9
27
Not enough data
8.0
27
Integration - AI Agent Builders
Not enough data
8.7
27
Not enough data
8.0
27
Not enough data
8.0
27
Not enough data
7.5
27
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
Azure Machine Learning and Vertex AI are categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Unique Categories
Azure Machine Learning has no unique categories
Vertex AI is categorized as Machine Learning and AI Agent Builders
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Vertex AI
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%
Computer Software
17.7%
Information Technology and Services
13.9%
Financial Services
7.0%
Retail
3.8%
Hospital & Health Care
3.4%
Other
54.2%
Alternatives
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
What is Google Cloud AI Platform used for?
2 Comments
KS
Google cloud AI Platform enables us to build Machine learning models, that works on any type and any size of data. Read more
What software libraries does cloud ML engine support?
2 Comments
JP
It's supporting approx all trending libraries.Read more
What is Google AI platform?
1 Comment
ZM
The Google AI Platform is a comprehensive set of tools and services provided by Google Cloud to develop, deploy, and manage artificial intelligence. It...Read more
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