Introducing G2.ai, the future of software buying.Try now
Compare MATLAB and Vertex AI 
Featured Products
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
ILUM
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
Lyzr.ai
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
Burai
Visit Website
At a Glance
Market Segments
Enterprise (42.0% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
MATLAB
Star Rating
(759)4.5 out of 5
Market Segments
Enterprise (42.0% of reviews)
Entry-Level Pricing
No pricing available
Learn more about MATLABVertex 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 Text Generation capabilities, scoring 8.3, while MATLAB lags slightly behind at 8.0. Reviewers mention that Vertex AI's integration with Google Cloud enhances its text generation features, making it a preferred choice for those focused on natural language tasks.
- Reviewers mention that MATLAB shines in Pre-Built Algorithms, achieving a score of 9.0 compared to Vertex AI's 8.4. Users appreciate MATLAB's extensive library of algorithms, which simplifies the process of implementing complex models, particularly in academic and enterprise settings.
- Users on G2 highlight that Vertex AI's Data Ingestion & Wrangling features are robust, scoring 8.2, but MATLAB slightly outperforms it with a score of 8.5. Reviewers say that MATLAB's data handling capabilities are particularly beneficial for users dealing with large datasets in engineering applications.
- G2 users report that both products are comparable in Natural Language Understanding, with both scoring 8.5. However, reviewers mention that Vertex AI's integration with other Google services provides a more seamless experience for users already embedded in the Google ecosystem.
- Users say that MATLAB's Ease of Setup is superior, scoring 8.6 compared to Vertex AI's 8.1. Reviewers mention that MATLAB's installation process is straightforward, making it more accessible for new users, especially in enterprise environments.
- Reviewers mention that Vertex AI's Product Direction is promising, with a score of 9.2, indicating a strong commitment to innovation. In contrast, MATLAB's score of 8.8 suggests a more stable but less aggressive approach to new features, which may appeal to users looking for reliability over rapid change.
Pricing
Entry-Level Pricing
MATLAB
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
MATLAB
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
9.0
666
8.6
359
Ease of Use
8.3
675
8.2
368
Ease of Setup
8.6
77
8.1
291
Ease of Admin
8.4
41
7.9
142
Quality of Support
8.6
619
8.1
335
Has the product been a good partner in doing business?
8.4
38
8.2
136
Product Direction (% positive)
8.8
664
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
Design
8.4
147
Not enough data
8.2
164
Not enough data
8.1
166
Not enough data
8.0
141
Not enough data
Tools
8.4
127
Not enough data
8.5
150
Not enough data
8.4
132
Not enough data
Work
8.8
159
Not enough data
8.9
161
Not enough data
8.7
145
Not enough data
8.3
135
Not enough data
8.4
141
Not enough data
Environment
8.4
163
Not enough data
8.7
162
Not enough data
8.4
138
Not enough data
8.8
177
Not enough data
System
8.5
74
8.2
170
Model Development
8.5
96
8.5
202
8.6
94
7.9
179
9.0
102
8.4
200
8.6
98
8.5
202
Model Development
8.7
79
8.3
165
Machine/Deep Learning Services
8.8
88
8.2
200
8.3
78
8.4
196
8.2
76
8.2
195
8.6
90
8.2
178
Machine/Deep Learning Services
8.5
69
8.5
165
8.7
77
8.5
163
Deployment
8.4
82
8.2
193
8.7
92
8.3
194
8.4
93
8.5
193
Generative AI
8.0
14
8.3
102
8.3
14
8.3
102
7.5
14
8.1
103
Agentic AI - Data Science and Machine Learning Platforms
8.7
10
8.1
34
8.7
10
7.9
34
7.8
10
7.7
34
9.0
10
7.9
34
7.8
10
8.5
34
7.7
10
7.8
34
8.0
10
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
Data Transformation
8.7
73
|
Verified
Not enough data
8.6
45
|
Verified
Not enough data
Connectivity
8.3
47
|
Verified
Not enough data
8.6
50
|
Verified
Not enough data
8.4
60
|
Verified
Not enough data
8.3
52
|
Verified
Not enough data
Operations
9.1
73
|
Verified
Not enough data
8.9
68
|
Verified
Not enough data
Feature Not Available
Not enough data
8.8
60
|
Verified
Not enough data
8.8
36
|
Verified
Not enough data
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
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
Categories
Categories
Shared Categories
MATLAB and Vertex AI are categorized as Data Science and Machine Learning Platforms
Unique Categories
MATLAB is categorized as Big Data Analytics and Simulation & CAE
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
MATLAB
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
Higher Education
15.6%
Research
12.9%
Computer Software
6.9%
Mechanical or Industrial Engineering
6.8%
Education Management
6.2%
Other
51.6%
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
Any good website to discuss about the matlab programming?
3 Comments
TD
Mathworks site would be better place since it's the official website of the producers of the matlabRead more
Can I use Matlab for free?
3 Comments
PC
You can use the trial version of MATLAB for free. Also, if you pursue any online courses e.g. from coursera, they allow you to use MATLAB for free for the...Read more
Is it possible to check the output of Simulink terminal if I am not keeping a scope?
2 Comments
MA
Yes, If you prepared from the beginning "to work space" block for all the Simulink model. You can view any variable you want. Read more
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
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
