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At a Glance
Market Segments
Enterprise (50.0% of reviews)
Small-Business (40.5% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
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 DataIBM watsonx.ai
Star Rating
(122)4.4 out of 5
Market Segments
Small-Business (40.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that IBM Cloud Pak for Data excels in "Data Ingestion & Wrangling" with a score of 9.5, making it a preferred choice for those needing robust data preparation capabilities. In contrast, IBM watsonx.ai received a lower score of 8.2 in this area, indicating it may not be as strong in handling complex data workflows.
- Reviewers mention that IBM watsonx.ai shines in "Ease of Use" with a score of 9.1, which is significantly higher than IBM Cloud Pak for Data's score of 8.1. This suggests that users find watsonx.ai more intuitive and user-friendly, making it suitable for small businesses or teams with less technical expertise.
- G2 users highlight that IBM Cloud Pak for Data offers superior "Scalability" with a score of 9.5, which is crucial for enterprises looking to grow their data operations. Conversely, IBM watsonx.ai's scalability score of 8.5 indicates it may not scale as effectively for larger organizations.
- Users on G2 report that IBM watsonx.ai provides better "Quality of Support" with a score of 8.8 compared to IBM Cloud Pak for Data's score of 8.3. This suggests that users may experience more responsive and helpful support when using watsonx.ai.
- Reviewers mention that IBM Cloud Pak for Data's "Customization" capabilities, particularly in "Custom VMs" and "Console Configuration," are rated at 8.0, which allows for tailored solutions. In contrast, IBM watsonx.ai does not emphasize customization to the same extent, which may limit flexibility for some users.
- Users say that IBM watsonx.ai's "Natural Language Processing" features are rated highly at 9.4, making it a strong choice for applications requiring advanced language understanding. In comparison, IBM Cloud Pak for Data's capabilities in this area are less emphasized, indicating a potential gap for users focused on AI-driven text analysis.
Pricing
Entry-Level Pricing
IBM Cloud Pak for Data
No pricing availableIBM watsonx.ai
No pricing availableFree Trial
IBM Cloud Pak for Data
No trial information availableIBM watsonx.ai
Free Trial is available
Ratings
Meets Requirements
8.5
47
8.8
77
Ease of Use
8.1
47
8.9
109
Ease of Setup
7.2
26
8.5
100
Ease of Admin
7.6
27
8.7
36
Quality of Support
8.3
42
8.8
76
Has the product been a good partner in doing business?
8.1
25
8.9
36
Product Direction (% positive)
8.8
47
9.9
79
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
9.1
9
Not enough data
8.5
9
Not enough data
7.8
9
Not enough data
8.7
9
Not enough data
8.7
9
Deployment
Not enough data
9.3
9
Not enough data
8.7
9
Not enough data
8.3
9
Not enough data
8.9
9
Not enough data
9.1
9
Management
Not enough data
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Operations
Not enough data
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Management
Not enough data
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Generative AI
Not enough data
9.1
9
Not enough data
9.3
9
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
31
Model Development
8.3
8
8.6
32
8.5
8
8.2
32
9.1
9
8.7
31
8.8
8
8.4
32
Model Development
8.5
12
8.5
32
Machine/Deep Learning Services
8.7
9
Feature Not Available
9.4
8
8.9
32
9.6
8
8.6
32
9.0
7
8.1
32
Machine/Deep Learning Services
9.2
8
8.5
32
9.3
7
8.8
32
Deployment
9.3
9
8.2
32
9.3
9
8.6
32
8.9
9
8.8
32
Generative AI
8.3
5
8.8
31
8.3
5
8.8
31
8.3
5
Feature Not Available
Agentic AI - Data Science and Machine Learning Platforms
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
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
Data Type
Not enough data
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Synthesis Type
Not enough data
9.0
12
Not enough data
9.2
12
Data Transformation
Not enough data
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
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.3
7
Not enough data
8.8
7
Not enough data
9.3
7
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.3
7
Not enough data
8.6
7
Not enough data
8.3
7
Integration and Extensibility - Generative AI Infrastructure
Not enough data
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Security and Compliance - Generative AI Infrastructure
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Usability and Support - Generative AI Infrastructure
Not enough data
9.3
7
Not enough data
9.0
7
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
Not enough data
9.0
21
Learning - Machine Learning
Not enough data
9.2
22
Not enough data
9.1
22
Not enough data
9.0
21
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
6
Not enough data
8.1
6
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
8.1
6
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
6
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
8.3
6
Not enough data
8.6
6
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
8.6
6
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.6
6
Not enough data
8.9
6
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.4
6
Not enough data
9.2
6
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
6
Customization - AI Agent Builders
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
9.0
7
Functionality - AI Agent Builders
Not enough data
8.6
7
Not enough data
9.0
7
Not enough data
9.3
7
Not enough data
8.8
7
Data and Analytics - AI Agent Builders
Not enough data
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Integration - AI Agent Builders
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
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 IBM watsonx.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)
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, Machine Learning, AI Content Creation Platforms, Large Language Model Operationalization (LLMOps), 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.)
IBM watsonx.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%
Information Technology and Services
18.9%
Computer Software
11.7%
Consulting
7.2%
Banking
6.3%
Marketing and Advertising
5.4%
Other
50.5%
Alternatives
Discussions
IBM Cloud Pak for Data has no discussions with answers
IBM watsonx.ai has no discussions with answers
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