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Compare PyTorch and Vertex AI 
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At a Glance
Market Segments
Small-Business (42.9% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
PyTorch
Star Rating
(22)4.5 out of 5
Market Segments
Small-Business (42.9% of reviews)
Entry-Level Pricing
No pricing available
Learn more about PyTorchVertex 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 offers a solid integration with Google Cloud services, making it a great choice for businesses already using the Google ecosystem, while PyTorch is praised for its flexibility and extensive community support, particularly in research environments.
- Reviewers mention that Vertex AI's ease of use is slightly lower than PyTorch's, with scores of 8.2 compared to 8.6, indicating that while Vertex AI is user-friendly, PyTorch may provide a more intuitive experience for developers familiar with machine learning frameworks.
- G2 users highlight that Vertex AI has a strong support system, scoring 8.2 in quality of support, whereas PyTorch, with a score of 7.9, may not offer the same level of dedicated support, which can be crucial for businesses needing quick resolutions.
- Users on G2 report that Vertex AI has a higher score in meeting requirements (8.6) compared to PyTorch (9.3), suggesting that PyTorch may better cater to specific advanced needs, particularly for those focused on deep learning projects.
- Reviewers mention that Vertex AI's product direction is rated positively at 9.1, but PyTorch shines with a perfect score of 10.0, indicating a strong commitment to innovation and user feedback in its development roadmap.
- Users say that while both products cater to small businesses, PyTorch has a slightly larger market segment presence (45.0% of reviews) compared to Vertex AI (38.0%), suggesting a broader appeal among smaller enterprises looking for machine learning solutions.
Pricing
Entry-Level Pricing
PyTorch
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
PyTorch
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
9.2
17
8.6
359
Ease of Use
8.6
18
8.2
368
Ease of Setup
Not enough data
8.1
291
Ease of Admin
Not enough data
7.9
142
Quality of Support
7.9
17
8.1
335
Has the product been a good partner in doing business?
Not enough data
8.2
136
Product Direction (% positive)
10.0
17
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
Not enough data
8.2
170
Model Development
Not enough data
8.5
202
Not enough data
7.9
179
Not enough data
8.4
200
Not enough data
8.5
202
Model Development
Not enough data
8.3
165
Machine/Deep Learning Services
Not enough data
8.2
200
Not enough data
8.4
196
Not enough data
8.2
195
Not enough data
8.2
178
Machine/Deep Learning Services
Not enough data
8.5
165
Not enough data
8.5
163
Deployment
Not enough data
8.2
193
Not enough data
8.3
194
Not enough data
8.5
193
Generative AI
Not enough data
8.3
102
Not enough data
8.3
102
Not enough data
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
Core Functionality - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Handling - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Performance - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Usability - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Features - Artificial Neural Network
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
Agentic AI - Artificial Neural Network
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
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
PyTorch and Vertex AI are categorized as Machine Learning
Unique Categories
PyTorch is categorized as Artificial Neural Network
Reviews
Reviewers' Company Size
PyTorch
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
Computer Software
28.6%
Research
14.3%
Telecommunications
9.5%
Information Technology and Services
9.5%
Automotive
9.5%
Other
28.6%
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
Do you use Pytorch for? (e.g. NLP, computer vision, reinforcement learning, etc)
1 Comment
IB
All of the paradigms for DeepLearning are supported for development by PyTorch Read more
What can you do with PyTorch?
1 Comment
AG
PyTorch is mostly used to train large ML/DL models.Read more
PyTorch 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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