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At a Glance
Market Segments
Small-Business (40.2% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
IBM watsonx.ai
Star Rating
(123)4.4 out of 5
Market Segments
Small-Business (40.2% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiVertex 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 offers a robust Template Library with a score of 9.0, which allows for quick deployment of machine learning models. In contrast, IBM watsonx.ai's library scored lower at 8.0, indicating that while it has templates, they may not be as comprehensive or user-friendly.
- Reviewers mention that IBM watsonx.ai excels in AI Inference Speed, scoring 9.4 compared to Vertex AI's 8.5. This suggests that users experience faster response times when deploying models, which is crucial for real-time applications.
- G2 users highlight that IBM watsonx.ai has superior Collaboration features, scoring 9.4 versus Vertex AI's 8.3. This indicates that teams using IBM watsonx.ai find it easier to work together on projects, enhancing productivity and communication.
- Users on G2 report that Vertex AI shines in Model Comparison Dashboard functionality, achieving a score of 9.3. This feature allows users to easily evaluate different models, which is essential for optimizing performance. IBM watsonx.ai, while still strong, scored slightly lower at 9.0.
- Reviewers mention that IBM watsonx.ai provides better Data Privacy Compliance tools, scoring 9.0 compared to Vertex AI's 8.0. This is particularly important for businesses that handle sensitive data and need to adhere to strict regulations.
- Users say that Vertex AI's AI Cost per API Call is competitive, scoring 8.2, which is beneficial for small businesses looking to manage costs effectively. IBM watsonx.ai scored slightly higher at 8.3, but the difference is minimal, indicating both platforms are reasonably priced for API usage.
Pricing
Entry-Level Pricing
IBM watsonx.ai
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 watsonx.ai
Free Trial is available
Vertex AI
Free Trial is available
Ratings
Meets Requirements
8.8
78
8.6
359
Ease of Use
8.8
110
8.2
368
Ease of Setup
8.5
101
8.1
291
Ease of Admin
8.7
36
7.9
141
Quality of Support
8.8
77
8.1
335
Has the product been a good partner in doing business?
8.9
36
8.2
135
Product Direction (% positive)
9.9
80
9.2
353
Features by Category
Deployment
9.1
9
8.3
73
8.5
9
8.1
74
7.8
9
8.3
74
8.7
9
8.3
70
8.7
9
8.8
70
Deployment
9.3
9
8.4
73
8.7
9
8.3
72
8.3
9
8.4
71
8.9
9
8.5
71
9.1
9
8.7
69
Management
8.0
9
8.3
70
8.5
9
8.5
69
8.5
9
8.0
69
9.3
9
8.1
69
Operations
9.1
9
8.2
69
8.7
9
8.4
70
9.3
9
8.3
70
Management
8.5
9
8.1
68
9.0
8
8.4
69
8.5
8
8.3
68
Generative AI
9.1
9
8.2
34
9.3
9
8.4
34
System
8.2
31
8.2
170
Model Development
8.6
32
8.4
202
8.2
32
7.9
179
8.7
31
8.4
200
8.4
32
8.5
202
Model Development
8.5
32
8.2
165
Machine/Deep Learning Services
Feature Not Available
8.2
200
8.9
32
8.4
196
8.6
32
8.2
195
8.1
32
8.2
178
Machine/Deep Learning Services
8.5
32
8.5
165
8.8
32
8.4
163
Deployment
8.2
32
8.2
193
8.6
32
8.3
194
8.8
32
8.5
193
Generative AI
8.8
31
8.3
102
8.8
31
8.2
102
Feature Not Available
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
Data Type
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Not enough data
Synthesis Type
9.0
12
Not enough data
9.2
12
Not enough data
Data Transformation
8.6
12
Not enough data
9.3
12
Not enough data
9.7
12
Not enough data
9.2
12
Not enough data
9.2
12
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.4
8
8.9
28
9.0
8
8.6
28
9.4
8
8.5
28
Cost and Efficiency - Generative AI Infrastructure
7.9
8
8.2
28
8.6
7
7.8
28
8.3
7
7.9
28
Integration and Extensibility - Generative AI Infrastructure
9.5
7
8.4
28
8.6
7
8.1
28
8.8
7
8.3
28
Security and Compliance - Generative AI Infrastructure
8.3
7
8.6
28
8.8
7
8.5
28
8.6
7
8.9
28
Usability and Support - Generative AI Infrastructure
9.3
7
8.2
28
9.0
7
8.3
28
Content Generation - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - Machine Learning
9.0
21
8.5
67
Learning - Machine Learning
9.2
22
8.5
66
9.1
22
8.3
65
9.0
21
8.8
66
Prompt Engineering - Large Language Model Operationalization (LLMOps)
9.2
6
8.8
22
8.1
6
8.9
22
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.9
6
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
8.9
6
9.2
22
Custom Training - Large Language Model Operationalization (LLMOps)
8.3
7
9.0
22
Application Development - Large Language Model Operationalization (LLMOps)
8.3
6
9.2
22
Model Deployment - Large Language Model Operationalization (LLMOps)
8.1
7
9.1
22
8.8
7
8.7
21
Guardrails - Large Language Model Operationalization (LLMOps)
9.4
6
9.0
21
8.6
6
8.8
21
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.6
6
8.7
21
8.9
6
9.0
21
Security - Large Language Model Operationalization (LLMOps)
9.4
6
9.1
22
9.2
6
8.9
22
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
6
8.9
22
Customization - AI Agent Builders
9.0
8
8.5
27
9.2
8
7.6
27
9.0
7
8.3
26
Functionality - AI Agent Builders
8.6
7
8.1
27
9.2
8
7.3
27
9.3
7
8.2
26
8.8
7
7.2
27
Data and Analytics - AI Agent Builders
9.0
7
7.7
25
8.8
7
7.9
27
9.0
7
8.0
27
Integration - AI Agent Builders
9.2
8
8.7
27
9.0
7
8.0
27
9.0
7
8.0
27
8.6
7
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
IBM watsonx.ai and Vertex AI are categorized as MLOps Platforms, Data Science and Machine Learning Platforms, Machine Learning, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), AI Agent Builders, and Low-Code Machine Learning Platforms
Unique Categories
IBM watsonx.ai is categorized as Synthetic Data and AI Content Creation Platforms
Vertex AI has no unique categories
Reviews
Reviewers' Company Size
IBM watsonx.ai
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
18.8%
Computer Software
11.6%
Consulting
7.1%
Financial Services
6.3%
Banking
6.3%
Other
50.0%
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 watsonx.ai has no discussions with answers
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