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At a Glance
Market Segments
Enterprise (50.0% of reviews)
Small-Business (41.0% 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
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataVertex 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 its AI Model Training Scalability with a score of 8.5, allowing for efficient handling of large datasets, while IBM Cloud Pak for Data also performs well with a score of 8.8, but users mention that its Data Ingestion & Wrangling capabilities are particularly robust, scoring 9.5.
- Reviewers mention that Vertex AI's Ease of Setup is rated at 8.2, making it user-friendly for new adopters, whereas IBM Cloud Pak for Data has a lower score of 7.2, indicating a steeper learning curve for setup.
- G2 users highlight that Vertex AI offers strong Language Flexibility with a score of 8.4, which is crucial for diverse development environments, while IBM Cloud Pak for Data matches this with an 8.6, but users report that its Framework Flexibility is equally impressive, scoring 8.3.
- Users on G2 note that Vertex AI's Quality of Support is rated at 8.2, which is slightly lower than IBM Cloud Pak for Data's score of 8.3, suggesting that users may find better assistance with IBM's platform.
- Reviewers mention that Vertex AI shines in Scalability with a score of 8.9, making it suitable for growing businesses, while IBM Cloud Pak for Data also scores well at 8.9, but users report that its Managed Service feature is rated higher at 9.3, indicating a more reliable service model.
- Users say that Vertex AI's Model Development features, particularly its Pre-Built Algorithms scoring 8.4, are beneficial for rapid prototyping, while IBM Cloud Pak for Data's Pre-Built Algorithms score of 9.1 is noted for providing a wider range of options for developers.
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
Free Trial is available
Ratings
Meets Requirements
8.5
47
8.6
359
Ease of Use
8.1
47
8.2
368
Ease of Setup
7.2
26
8.1
291
Ease of Admin
7.6
27
7.9
141
Quality of Support
8.3
42
8.1
335
Has the product been a good partner in doing business?
8.1
25
8.2
135
Product Direction (% positive)
8.8
47
9.2
353
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
8.3
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
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
170
Model Development
8.3
8
8.4
202
8.5
8
7.9
179
9.1
9
8.4
200
8.8
8
8.5
202
Model Development
8.5
12
8.2
165
Machine/Deep Learning Services
8.7
9
8.2
200
9.4
8
8.4
196
9.6
8
8.2
195
9.0
7
8.2
178
Machine/Deep Learning Services
9.2
8
8.5
165
9.3
7
8.4
163
Deployment
9.3
9
8.2
193
9.3
9
8.3
194
8.9
9
8.5
193
Generative AI
8.3
5
8.3
102
8.3
5
8.2
102
8.3
5
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 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
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
8.3
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%
Information Technology and Services
8.6%
Banking
8.6%
Financial Services
5.7%
Education Management
5.7%
Other
58.6%
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 Cloud Pak for Data 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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