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At a Glance
Market Segments
Enterprise (51.3% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
IBM Watson Studio
Star Rating
(164)4.2 out of 5
Market Segments
Enterprise (51.3% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioVertex AI
Star Rating
(592)4.3 out of 5
Market Segments
Small-Business (41.0% 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.
- Users report that Vertex AI excels in "Ease of Data Connectivity" with a score of 9.3, making it easier to connect various data sources compared to IBM Watson Studio, which scored 8.8. This feature is particularly beneficial for small businesses looking to integrate diverse datasets seamlessly.
- Reviewers mention that IBM Watson Studio shines in "Ease of Deployment," achieving a remarkable score of 9.8, while Vertex AI scored 8.4. Users appreciate the streamlined deployment process in Watson Studio, which is crucial for enterprises needing quick implementation.
- G2 users highlight that Vertex AI offers superior "No-Code" capabilities with a score of 9.7, allowing users without programming skills to build models easily. In contrast, IBM Watson Studio's no-code features are less emphasized, which may limit accessibility for non-technical users.
- Reviewers say that IBM Watson Studio's "Model Training" capabilities are robust, scoring 9.0, compared to Vertex AI's 8.5. Users appreciate the extensive support for various algorithms and training methods in Watson Studio, making it a preferred choice for data scientists.
- Users on G2 report that Vertex AI's "Data Quality and Cleansing" features are highly rated at 9.2, which is essential for ensuring accurate data analysis. In comparison, IBM Watson Studio's score in this area is slightly lower, indicating that Vertex AI may provide a more comprehensive solution for data preparation.
- Reviewers mention that IBM Watson Studio's "Monitoring" capabilities are exceptional, with a score of 9.8, which is critical for ongoing model performance evaluation. Vertex AI, while competent, scored 8.6, suggesting that Watson Studio may offer more advanced tools for tracking model effectiveness over time.
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
121
8.6
359
Ease of Use
8.0
122
8.2
368
Ease of Setup
7.6
100
8.1
291
Ease of Admin
7.8
95
7.9
141
Quality of Support
8.2
113
8.1
335
Has the product been a good partner in doing business?
8.0
94
8.2
135
Product Direction (% positive)
8.5
115
9.2
353
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
8.3
73
9.2
8
8.1
74
9.0
8
8.3
74
9.4
8
8.3
70
8.8
8
8.8
70
Deployment
9.0
8
8.4
73
8.8
8
8.3
72
8.8
8
8.4
71
9.4
8
8.5
71
9.2
8
8.7
69
Management
9.3
7
8.3
70
9.6
8
8.5
69
9.0
7
8.0
69
9.0
8
8.1
69
Operations
9.0
8
8.2
69
9.0
8
8.4
70
9.3
7
8.3
70
Management
9.5
7
8.1
68
9.4
8
8.4
69
8.8
7
8.3
68
Generative AI
Not enough data
8.2
34
Not enough data
8.4
34
System
9.0
12
8.2
170
Model Development
8.5
33
8.4
202
8.8
34
7.9
179
8.5
35
8.4
200
8.3
36
8.5
202
Model Development
9.4
13
8.2
165
Machine/Deep Learning Services
8.5
27
8.2
200
8.5
34
8.4
196
Feature Not Available
8.2
195
8.6
28
8.2
178
Machine/Deep Learning Services
8.9
12
8.5
165
9.0
12
8.4
163
Deployment
8.5
32
8.2
193
8.6
33
8.3
194
8.6
30
8.5
193
Generative AI
Not enough data
8.3
102
Not enough data
8.2
102
Not enough data
8.1
103
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
34
Not enough data
7.8
34
Not enough data
7.7
34
Not enough data
7.8
34
Not enough data
8.4
34
Not enough data
7.8
34
Not enough data
7.9
34
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
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
8.9
23
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Not enough data
8.9
22
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
22
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
22
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
22
Not enough data
8.7
21
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
21
Not enough data
8.8
21
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
21
Not enough data
9.0
21
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
8.9
22
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
22
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
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
15.8%
Computer Software
13.3%
Telecommunications
8.2%
Banking
7.6%
Education Management
5.7%
Other
49.4%
Computer Software
17.5%
Information Technology and Services
13.9%
Financial Services
7.0%
Retail
3.8%
Hospital & Health Care
3.4%
Other
54.4%
Alternatives
Discussions
IBM Watson Studio has no discussions with answers
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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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