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Compare IBM watsonx.ai and Red Hat OpenShift Data Science 
Featured Products
At a Glance
Market Segments
Small-Business (41.0% of reviews)
Mid-Market (44.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
IBM watsonx.ai
Star Rating
(135)4.4 out of 5
Market Segments
Small-Business (41.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiRed Hat OpenShift Data Science
Star Rating
(25)4.4 out of 5
Market Segments
Mid-Market (44.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about Red Hat OpenShift Data ScienceSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that IBM watsonx.ai excels in user-friendliness, particularly with its AI studio, which allows users to create chatbots efficiently using pre-trained models. This feature has been highlighted as a significant time-saver for many users, making it a strong choice for those looking for a no-code solution.
- Users say that Red Hat OpenShift Data Science offers exceptional scalability and flexibility, especially in finance-related applications. The ability to containerize data science workloads ensures reliable performance, which is crucial for handling large datasets and complex algorithms.
- Reviewers mention that IBM watsonx.ai provides robust customization options for creating AI assistants, allowing for detailed attention to specific needs. This level of customization has been praised by users who appreciate the platform's adaptability to their unique requirements.
- According to verified reviews, Red Hat OpenShift Data Science is noted for its strong support and documentation, which users find extremely helpful. This aspect contributes to a smoother onboarding experience, particularly for teams new to data science workflows.
- G2 reviewers highlight that while both platforms have similar star ratings, IBM watsonx.ai has a significantly larger number of reviews, indicating a broader user base and potentially more reliable insights into its performance and capabilities.
- Users report that Red Hat OpenShift Data Science's containerization feature provides a unique approach to managing data science workflows, allowing teams to package financial models and algorithms effectively. This capability is seen as a major advantage for organizations focused on consistency and reproducibility in their data science projects.
Pricing
Entry-Level Pricing
IBM watsonx.ai
No pricing availableRed Hat OpenShift Data Science
No pricing availableFree Trial
IBM watsonx.ai
Free Trial is available
Red Hat OpenShift Data Science
No trial information availableRatings
Meets Requirements
8.8
87
8.8
23
Ease of Use
8.8
120
8.5
23
Ease of Setup
8.4
111
Not enough data
Ease of Admin
8.7
38
Not enough data
Quality of Support
8.7
85
8.6
21
Has the product been a good partner in doing business?
8.9
38
Not enough data
Product Direction (% positive)
9.9
88
10.0
23
Features by Category
Deployment
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
Not enough data
Deployment
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
Not enough data
Management
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Not enough data
Operations
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Not enough data
Management
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
Generative AI
9.1
9
Not enough data
9.3
9
Not enough data
System
8.2
31
8.9
22
Model Development
8.7
33
8.8
23
8.3
35
8.8
23
8.7
31
8.7
23
8.2
33
8.6
23
Model Development
8.5
32
8.8
23
Machine/Deep Learning Services
Feature Not Available
8.5
22
8.9
32
8.3
20
8.6
32
8.6
20
8.1
32
8.3
20
Machine/Deep Learning Services
8.5
32
8.6
20
8.8
32
8.7
21
Deployment
8.2
32
8.6
22
8.6
32
8.8
22
8.8
32
8.5
22
Generative AI
8.8
31
8.3
5
8.8
31
8.7
5
Feature Not Available
8.7
5
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 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
Not enough data
9.0
8
Not enough data
9.4
8
Not enough data
Cost and Efficiency - Generative AI Infrastructure
7.9
8
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
Integration and Extensibility - Generative AI Infrastructure
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Not enough data
Security and Compliance - Generative AI Infrastructure
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Not enough data
Usability and Support - Generative AI Infrastructure
9.0
8
Not enough data
9.0
7
Not enough data
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
Not enough data
Learning - Machine Learning
9.2
23
Not enough data
9.1
22
Not enough data
9.0
21
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
8.7
15
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
9.1
9
Not enough data
8.1
6
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
8.3
10
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
8.3
6
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
7.4
13
Not enough data
8.7
9
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
8.8
10
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.8
7
Not enough data
8.9
6
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
9.2
6
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Customization - AI Agent Builders
9.0
8
Not enough data
9.2
8
Not enough data
9.0
7
Not enough data
Functionality - AI Agent Builders
8.6
7
Not enough data
9.2
8
Not enough data
9.3
7
Not enough data
8.8
7
Not enough data
Data and Analytics - AI Agent Builders
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
Integration - AI Agent Builders
9.2
8
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
Not enough data
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 Red Hat OpenShift Data Science are categorized as Data Science and Machine Learning Platforms
Unique Categories
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, Machine Learning, AI Content Creation Platforms, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), AI Agent Builders, and Low-Code Machine Learning Platforms
Red Hat OpenShift Data Science 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.)
Red Hat OpenShift Data Science
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
19.7%
Computer Software
11.5%
Consulting
7.4%
Financial Services
6.6%
Banking
5.7%
Other
49.2%
Market Research
32.0%
Marketing and Advertising
20.0%
Information Technology and Services
8.0%
Computer Software
8.0%
Transportation/Trucking/Railroad
4.0%
Other
28.0%
Alternatives
Discussions
IBM watsonx.ai has no discussions with answers
Red Hat OpenShift Data Science has no discussions with answers
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