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- IBM watsonx.ai-vs-Red Hat OpenShift Data Science
Compare IBM watsonx.ai and Red Hat OpenShift Data Science 
At a Glance
Market Segments
Small-Business (37.8% of reviews)
Mid-Market (44.0% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
IBM watsonx.ai
Star Rating
(79)4.5 out of 5
Market Segments
Small-Business (37.8% of reviews)
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)
Entry-Level Pricing
No pricing available
Learn more about Red Hat OpenShift Data ScienceSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Red Hat OpenShift Data Science excels in Language Flexibility with a score of 9.0, allowing for a diverse range of programming languages, while IBM watsonx.ai, although strong, scores slightly lower at 8.8. Reviewers mention that this flexibility is crucial for teams working with multiple languages.
- Reviewers mention that IBM watsonx.ai shines in Ease of Use with a score of 9.1 compared to Red Hat OpenShift Data Science's 8.5. Users on G2 appreciate the intuitive interface of IBM watsonx.ai, which simplifies the onboarding process for new users.
- G2 users highlight that Red Hat OpenShift Data Science offers superior Data Ingestion & Wrangling capabilities, scoring 8.9, which reviewers say is essential for handling large datasets efficiently. In contrast, IBM watsonx.ai scores 8.2, indicating room for improvement in this area.
- Users say that IBM watsonx.ai provides better Model Training features, with a score of 8.5, which reviewers mention includes robust pre-built algorithms that streamline the training process. Red Hat OpenShift Data Science scores 8.6, but users feel it lacks some of the advanced features found in IBM's offering.
- Reviewers mention that Red Hat OpenShift Data Science has a strong focus on Scalability, scoring 9.0, which users report is beneficial for growing businesses. IBM watsonx.ai also scores well at 8.5, but users feel that Red Hat's platform is more adaptable to increasing workloads.
- Users on G2 report that IBM watsonx.ai excels in Quality of Support, with a score of 8.8, which reviewers say is backed by responsive customer service and extensive documentation. Red Hat OpenShift Data Science scores slightly lower at 8.6, indicating that while support is good, it may not be as comprehensive as IBM's.
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
8.9
63
8.8
23
Ease of Use
9.1
72
8.5
23
Ease of Setup
9.1
64
Not enough data
Ease of Admin
8.6
32
Not enough data
Quality of Support
8.8
62
8.6
21
Has the product been a good partner in doing business?
8.9
32
Not enough data
Product Direction (% positive)
9.8
63
10.0
23
Features by Category
Deployment
9.2
8
Not enough data
8.3
8
Not enough data
8.1
8
Not enough data
9.2
8
Not enough data
9.2
8
Not enough data
Deployment
9.2
8
Not enough data
8.5
8
Not enough data
8.3
8
Not enough data
9.2
8
Not enough data
9.6
8
Not enough data
Management
8.3
8
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
9.4
8
Not enough data
Operations
9.0
8
Not enough data
8.5
8
Not enough data
9.4
8
Not enough data
Management
8.5
8
Not enough data
9.0
7
Not enough data
8.6
7
Not enough data
Generative AI
9.2
8
Not enough data
9.4
8
Not enough data
System
8.3
28
8.9
22
8.8
29
9.1
23
8.2
29
8.7
23
Model Development
8.6
29
8.8
23
8.2
29
8.8
23
8.7
28
8.7
23
8.3
29
8.6
23
Model Development
8.6
29
8.5
22
8.6
29
8.6
22
8.4
29
8.8
23
Machine/Deep Learning Services
Feature Not Available
8.5
22
8.9
29
8.3
20
8.6
29
8.6
20
8.1
29
8.3
20
Machine/Deep Learning Services
Feature Not Available
8.8
22
8.4
29
8.6
20
8.7
29
8.6
20
8.7
29
8.7
21
Deployment
8.2
29
8.6
22
8.6
29
8.8
22
8.6
29
8.5
22
Deployment
8.5
28
8.8
23
8.8
28
8.7
23
8.6
28
8.6
23
Generative AI
8.8
28
8.3
5
8.8
28
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
9.2
11
Not enough data
Feature Not Available
Not enough data
8.8
10
Not enough data
Synthesis Type
9.2
10
Not enough data
9.2
10
Not enough data
Data Transformation
8.8
10
Not enough data
9.7
10
Not enough data
9.7
10
Not enough data
9.2
10
Not enough data
9.2
10
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
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
8.9
18
Not enough data
Learning - Machine Learning
9.1
19
Not enough data
9.1
19
Not enough data
9.1
18
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Customization - AI Agent Builders
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Functionality - AI Agent Builders
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 and Analytics - AI Agent Builders
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - AI Agent Builders
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 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), and AI Agent Builders
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
21.6%
Computer Software
12.2%
Marketing and Advertising
8.1%
Market Research
8.1%
Consulting
5.4%
Other
44.6%
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%
Most Helpful Reviews
IBM watsonx.ai
Most Helpful Favorable Review
Rizwan A.
Verified User in Computer & Network Security
It collaborates with various generative AI tools and can be deployed on hybrid environment
Most Helpful Critical Review
Verified User in Consulting
Regarding integration, running wx.ai in a python notebook is still very slow when compared to having a locally hosted LLM. Building more complex models and adding up governance to the equation makes the interface less friendly.
Red Hat OpenShift Data Science
Most Helpful Favorable Review
Verified User in Automotive
Open source, no vendor lock-in and it is also very secure platform
Most Helpful Critical Review
Rizwan A.
Verified User in Computer & Network Security
It collaborates with various generative AI tools and can be deployed on hybrid environment
Verified User in Automotive
Open source, no vendor lock-in and it is also very secure platform
Verified User in Consulting
Regarding integration, running wx.ai in a python notebook is still very slow when compared to having a locally hosted LLM. Building more complex models and adding up governance to the equation makes the interface less friendly.
Alternatives
Discussions
IBM watsonx.ai has no discussions with answers
Red Hat OpenShift Data Science has no discussions with answers
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