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Compare IBM Watson Studio and Vertex AI 
Featured Products
At a Glance
Market Segments
Enterprise (50.9% of reviews)
Small-Business (41.3% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
IBM Watson Studio
Star Rating
(166)4.2 out of 5
Market Segments
Enterprise (50.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioVertex AI
Star Rating
(641)4.3 out of 5
Market Segments
Small-Business (41.3% of reviews)
Pros & Cons
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.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users appreciating its ability to centralize the entire ML lifecycle—from data preparation to deployment and monitoring. This integration significantly reduces the effort needed to build, train, and deploy models.
- Users say that IBM Watson Studio offers a well-organized platform that supports a variety of data science and ML tasks. Reviewers highlight its easy integration with existing datasets, making it a solid choice for those looking for a low code/no code AI development environment.
- According to verified reviews, Vertex AI has a notably higher G2 Score, indicating greater overall user satisfaction. This is reflected in the positive feedback regarding its seamless integration with Google Cloud, which enhances the user experience for managing ML projects.
- Reviewers mention that while IBM Watson Studio provides a powerful AI/ML tool, some users have faced challenges with controlling the flow of execution in their projects. This has led to a search for alternatives, suggesting that Vertex AI may offer a more intuitive experience for managing workflows.
- Users highlight that Vertex AI's ease of setup and administration is a significant advantage, with many finding the onboarding process straightforward. In contrast, IBM Watson Studio's setup has been noted as less user-friendly, which could impact new users looking to get started quickly.
- G2 reviewers indicate that while both platforms have strong scalability features, Vertex AI's recent user feedback emphasizes its ability to handle complex ML tasks efficiently, whereas IBM Watson Studio's strengths lie in its flexibility and support for various frameworks, appealing more to enterprise-level users.
Pricing
Entry-Level Pricing
IBM Watson Studio
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
IBM Watson Studio
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
8.3
122
8.6
382
Ease of Use
8.0
123
8.2
393
Ease of Setup
7.6
101
8.1
316
Ease of Admin
7.8
95
7.9
147
Quality of Support
8.2
114
8.1
357
Has the product been a good partner in doing business?
8.0
94
8.3
141
Product Direction (% positive)
8.5
116
9.2
376
Features by Category
Data Source Access
9.0
13
Not enough data
9.3
12
Not enough data
9.2
14
Not enough data
Data Interaction
9.0
14
Not enough data
9.2
12
Not enough data
9.4
12
Not enough data
9.1
13
Not enough data
9.2
12
Not enough data
9.2
13
Not enough data
9.1
13
Not enough data
9.6
12
Not enough data
Data Exporting
9.4
12
Not enough data
9.2
12
Not enough data
9.2
12
Not enough data
Generative AI
Not enough data
Not enough data
Deployment
8.8
8
8.4
76
9.2
8
8.1
77
9.0
8
8.3
76
9.4
8
8.4
76
8.8
8
8.8
74
Deployment
9.0
8
8.5
75
8.8
8
8.3
73
8.8
8
8.4
72
9.4
8
8.6
74
9.2
8
8.7
71
Management
9.3
7
8.2
71
9.6
8
8.5
72
9.0
7
8.0
71
9.0
8
8.1
70
Operations
9.0
8
8.2
70
9.0
8
8.5
71
9.3
7
8.3
71
Management
9.5
7
8.1
69
9.4
8
8.4
72
8.8
7
8.3
70
Generative AI
Not enough data
8.3
36
Not enough data
8.5
36
System
9.1
13
8.2
173
Model Development
8.6
34
8.5
206
8.9
35
7.9
181
8.5
36
8.4
204
8.4
37
8.5
209
Model Development
9.4
13
8.2
167
Machine/Deep Learning Services
8.6
28
8.3
203
8.5
35
8.5
201
Feature Not Available
8.2
198
8.6
28
8.3
180
Machine/Deep Learning Services
8.9
12
8.5
167
9.0
12
8.5
164
Deployment
8.5
32
8.3
208
8.6
33
8.3
199
8.7
31
8.5
203
Generative AI
Not enough data
8.3
108
Not enough data
8.3
105
Not enough data
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.2
37
Not enough data
7.8
37
Not enough data
7.7
37
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Setup
8.6
7
Not enough data
8.3
7
Not enough data
9.7
6
Not enough data
Data
8.6
7
Not enough data
8.6
7
Not enough data
Analysis
9.7
6
Not enough data
8.1
7
Not enough data
8.1
7
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
Customization
9.0
7
Not enough data
8.1
7
Not enough data
9.2
6
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.0
31
Not enough data
8.7
31
Not enough data
8.6
31
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.0
34
Not enough data
7.7
31
Not enough data
8.1
30
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.5
30
Not enough data
8.3
31
Not enough data
8.4
30
Security and Compliance - Generative AI Infrastructure
Not enough data
8.7
30
Not enough data
8.4
31
Not enough data
8.9
30
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
31
Not enough data
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
29
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
Statistical Tool
8.0
14
Not enough data
8.4
15
Not enough data
8.1
15
Not enough data
Data Analysis
8.7
15
Not enough data
9.0
14
Not enough data
Decision Making
8.6
14
Not enough data
8.6
15
Not enough data
8.3
13
Not enough data
8.7
14
Not enough data
Generative AI
9.3
5
Not enough data
8.3
5
Not enough data
Categories
Categories
Shared Categories
IBM Watson Studio and Vertex AI are categorized as Data Science and Machine Learning Platforms and MLOps Platforms
Unique Categories
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
IBM Watson Studio
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
15.7%
Computer Software
13.2%
Telecommunications
8.2%
Banking
7.5%
Education Management
5.7%
Other
49.7%
Computer Software
17.8%
Information Technology and Services
14.3%
Financial Services
6.9%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
Alternatives
Discussions
IBM Watson Studio has no 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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