Compare IBM Cloud Pak for Data and Vertex AI 
At a Glance
Market Segments
Enterprise (50.0% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
IBM Cloud Pak for Data
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataVertex AI
Star Rating
(652)4.3 out of 5
Market Segments
Small-Business (42.0% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
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 praising its ability to centralize the entire ML lifecycle. One user highlighted how it simplifies everything from data preparation to deployment, making it easier to build and train models.
- Users say that IBM Cloud Pak for Data is a vital tool for digital transformation, offering a comprehensive approach to data management and analytics. Reviewers appreciate its capability to unify siloed data, which helps in identifying trends and optimizing resource allocation.
- According to verified reviews, Vertex AI has a significantly higher overall satisfaction score, reflecting its strong performance in user experience and ease of setup. Users have noted the intuitive onboarding process that helps them get started quickly with the platform.
- Reviewers mention that while IBM Cloud Pak for Data has an intuitive user interface, it faces challenges in ease of setup compared to Vertex AI. Users have pointed out that the setup process can be more cumbersome, which may affect initial user experience.
- G2 reviewers highlight that Vertex AI's seamless integration with Google Cloud enhances its usability, allowing users to manage the full ML lifecycle in one place. This integration is particularly appreciated by those who rely on Google services for their data needs.
- Users report that while IBM Cloud Pak for Data offers strong support for data governance and analytics, it may not be as agile as Vertex AI in terms of model training and deployment. Some users feel that Vertex AI's focus on machine learning gives it an edge in these specific areas.
Pricing
Entry-Level Pricing
IBM Cloud Pak for Data
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 Cloud Pak for Data
No trial information availableVertex AI
No trial information availableRatings
Meets Requirements
8.5
47
8.6
389
Ease of Use
8.1
47
8.2
400
Ease of Setup
7.2
26
8.1
322
Ease of Admin
7.6
27
7.9
149
Quality of Support
8.3
42
8.1
364
Has the product been a good partner in doing business?
8.1
25
8.3
143
Product Direction (% positive)
8.8
47
9.2
383
Features by Category
Customization
8.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure
Not enough data
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
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
Deployment
Not enough data
8.4
76
Not enough data
8.1
78
Not enough data
8.3
76
Not enough data
8.4
76
Not enough data
8.8
75
Deployment
Not enough data
8.5
75
Not enough data
8.3
73
Not enough data
8.4
72
Not enough data
8.6
74
Not enough data
8.7
71
Management
Not enough data
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Operations
Not enough data
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Management
Not enough data
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Generative AI
Not enough data
8.4
37
Not enough data
8.6
37
Infrastructure Provision
7.7
8
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
8.3
6
Not enough data
8.5
8
Not enough data
7.9
8
Not enough data
8.3
8
Not enough data
8.3
7
Not enough data
Management
8.6
7
Not enough data
8.3
7
Not enough data
8.1
8
Not enough data
Functionality
8.3
8
Not enough data
System
9.5
11
8.2
173
Model Development
8.3
8
8.5
208
8.5
8
7.9
181
9.1
9
8.4
206
8.8
8
8.5
209
Model Development
8.5
12
8.2
167
Machine/Deep Learning Services
8.7
9
8.3
203
9.4
8
8.5
202
9.6
8
8.2
200
9.0
7
8.3
181
Machine/Deep Learning Services
9.2
8
8.5
167
9.3
7
8.5
166
Deployment
9.3
9
8.3
213
9.3
9
8.3
203
8.9
9
8.6
207
Generative AI
8.3
5
8.3
110
8.3
5
8.3
106
8.3
5
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
38
Not enough data
7.8
37
Not enough data
7.7
38
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Data Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
Analytics
Not enough data
Not enough data
Security
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Fabric
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
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.0
31
Not enough data
8.7
32
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
32
Not enough data
8.5
31
Security and Compliance - Generative AI Infrastructure
Not enough data
8.7
30
Not enough data
8.3
32
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
30
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
8.3
5
Not enough data
8.8
8
Not enough data
8.6
7
Not enough data
Data Analysis
8.7
9
Not enough data
9.0
7
Not enough data
Decision Making
8.3
6
Not enough data
8.8
7
Not enough data
8.8
8
Not enough data
8.3
8
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Data Transformation
8.5
27
|
Verified
Not enough data
9.1
15
|
Verified
Not enough data
Connectivity
8.0
23
|
Verified
Not enough data
8.6
22
|
Verified
Not enough data
8.1
25
|
Verified
Not enough data
8.7
24
|
Verified
Not enough data
Operations
8.7
26
|
Verified
Not enough data
8.9
25
|
Verified
Not enough data
8.4
23
|
Verified
Not enough data
8.8
24
|
Verified
Not enough data
8.7
13
|
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
IBM Cloud Pak for Data and Vertex AI are categorized as Data Science and Machine Learning Platforms and Generative AI Infrastructure
Unique Categories
IBM Cloud Pak for Data is categorized as Data Fabric, Virtual Private Cloud (VPC), Big Data Analytics, Predictive Analytics, and Infrastructure as a Service (IaaS)
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
IBM Cloud Pak for Data
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
12.9%
Banking
8.6%
Information Technology and Services
8.6%
Education Management
5.7%
Financial Services
5.7%
Other
58.6%
Computer Software
17.9%
Information Technology and Services
14.3%
Financial Services
6.8%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
Alternatives
Discussions
IBM Cloud Pak for Data has no discussions with answers
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3 Comments
AO
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Cloud ML Engine (now part of Vertex AI) supports popular ML frameworks and libraries such as TensorFlow, PyTorch, Scikit‑learn, and XGBoost. This flexibility...Read more
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2 Comments
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