Compare Google Cloud AI Infrastructure and Vertex AI 
At a Glance
Market Segments
Small-Business (48.9% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Google Cloud AI Infrastructure
Star Rating
(45)4.5 out of 5
Market Segments
Small-Business (48.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Google Cloud AI InfrastructureVertex AI
Star Rating
(652)4.3 out of 5
Market Segments
Small-Business (42.0% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users appreciating its ability to centralize the entire ML lifecycle. One user highlighted how it simplifies everything from data preparation to deployment, making it a powerful tool for those looking to streamline their processes.
- Users say that Google Cloud AI Infrastructure is highly scalable and reliable for AI workloads, with seamless integration across various Google Cloud services. This makes it particularly appealing for enterprises that require robust infrastructure to support diverse AI applications.
- According to verified reviews, Vertex AI has a significantly higher overall satisfaction score, indicating that users feel more positively about their experience with the platform. Many users noted the intuitive design and ease of use, which helps reduce the effort needed to build and deploy models.
- Reviewers mention that while Google Cloud AI Infrastructure is enterprise-ready, it may not be as user-friendly for smaller businesses. Some users expressed that the complexity of setup and administration can be a barrier, especially for those without extensive technical expertise.
- G2 reviewers highlight the strong community support and documentation quality for both platforms, but Vertex AI users noted that its documentation could be more comprehensive. This suggests that while both products have solid support, there may be room for improvement in helping users navigate challenges.
- Users appreciate the integration capabilities of both platforms, but Vertex AI's seamless connection with Google Cloud services stands out. This integration allows users to manage their ML workflows more effectively, which is a significant advantage for those already invested in the Google ecosystem.
Pricing
Entry-Level Pricing
Google Cloud AI Infrastructure
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
Google Cloud AI Infrastructure
No trial information availableVertex AI
No trial information availableRatings
Meets Requirements
9.0
42
8.6
389
Ease of Use
9.0
44
8.2
400
Ease of Setup
9.1
39
8.1
322
Ease of Admin
9.2
14
7.9
149
Quality of Support
8.8
40
8.1
364
Has the product been a good partner in doing business?
9.2
14
8.3
143
Product Direction (% positive)
9.7
43
9.2
383
Features by Category
Deployment
Not enough data
8.4
76
Not enough data
8.1
78
Not enough data
8.3
76
Not enough data
8.4
76
Not enough data
8.8
75
Deployment
Not enough data
8.5
75
Not enough data
8.3
73
Not enough data
8.4
72
Not enough data
8.6
74
Not enough data
8.7
71
Management
Not enough data
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Operations
Not enough data
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Management
Not enough data
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Generative AI
Not enough data
8.4
37
Not enough data
8.6
37
System
Not enough data
8.2
173
Model Development
Not enough data
8.5
208
Not enough data
7.9
181
Not enough data
8.4
206
Not enough data
8.5
209
Model Development
Not enough data
8.2
167
Machine/Deep Learning Services
Not enough data
8.3
203
Not enough data
8.5
202
Not enough data
8.2
200
Not enough data
8.3
181
Machine/Deep Learning Services
Not enough data
8.5
167
Not enough data
8.5
166
Deployment
Not enough data
8.3
213
Not enough data
8.3
203
Not enough data
8.6
207
Generative AI
Not enough data
8.3
110
Not enough data
8.3
106
Not enough data
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
38
Not enough data
7.8
37
Not enough data
7.7
38
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Scalability and Performance - Generative AI Infrastructure
9.2
24
9.0
31
9.3
24
8.7
32
9.1
23
8.6
31
Cost and Efficiency - Generative AI Infrastructure
8.1
26
8.0
34
8.5
24
7.7
31
8.7
21
8.1
30
Integration and Extensibility - Generative AI Infrastructure
8.6
22
8.5
30
8.4
26
8.3
32
8.7
25
8.5
31
Security and Compliance - Generative AI Infrastructure
8.8
22
8.7
30
8.5
23
8.3
32
8.5
22
8.9
30
Usability and Support - Generative AI Infrastructure
8.5
22
8.2
31
8.6
22
8.5
31
Integration - Machine Learning
Not enough data
8.5
68
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
26
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
24
Not enough data
9.0
24
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.3
25
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
24
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
23
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
23
Not enough data
8.7
22
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
23
Not enough data
8.9
22
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
21
Not enough data
9.1
22
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
9.0
23
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
22
Customization - AI Agent Builders
Not enough data
8.6
28
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
7.8
26
Not enough data
7.9
27
Not enough data
8.1
28
Integration - AI Agent Builders
Not enough data
8.8
28
Not enough data
8.2
30
Not enough data
8.1
28
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
Google Cloud AI Infrastructure and Vertex AI are categorized as Generative AI Infrastructure
Unique Categories
Google Cloud AI Infrastructure has no unique categories
Reviews
Reviewers' Company Size
Google Cloud AI Infrastructure
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
22.2%
Computer Software
15.6%
Consulting
4.4%
Marketing and Advertising
4.4%
Leisure, Travel & Tourism
4.4%
Other
48.9%
Computer Software
17.9%
Information Technology and Services
14.3%
Financial Services
6.8%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
Alternatives
Discussions
Google Cloud AI Infrastructure has no discussions with answers
What is Google Cloud AI Platform used for?
3 Comments
AO
Vertex AI is Google Cloud’s managed machine learning platform. It’s used to build, train, and deploy ML models at scale. It unifies data, AutoML, and custom...Read more
What software libraries does cloud ML engine support?
3 Comments
AO
Cloud ML Engine (now part of Vertex AI) supports popular ML frameworks and libraries such as TensorFlow, PyTorch, Scikit‑learn, and XGBoost. This flexibility...Read more
What is Google AI platform?
2 Comments
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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