Compare Gemini Enterprise Agent Platform and Red Hat OpenShift Data Science 
At a Glance
Market Segments
Small-Business (42.2% of reviews)
Mid-Market (44.0% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
No pricing available
Gemini Enterprise Agent Platform
Star Rating
(652)4.3 out of 5
Market Segments
Small-Business (42.2% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Learn more about Gemini Enterprise Agent PlatformRed 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 Vertex AI excels in managing complex machine learning workflows, with users highlighting its ability to centralize the entire ML lifecycle. This integration simplifies tasks from data preparation to deployment, making it a favorite for those looking for an all-in-one solution.
- Users say that Red Hat OpenShift Data Science offers excellent performance and ease of use, particularly in containerization. Reviewers appreciate how this feature provides scalability and flexibility, especially in industries like finance where handling large datasets is crucial.
- According to verified reviews, Vertex AI has a significantly higher overall satisfaction score, reflecting its strong user base and recent positive feedback. Users frequently mention the platform's seamless integration with Google Cloud, which enhances their experience in managing ML projects.
- Reviewers mention that while Red Hat OpenShift Data Science has strong documentation and support, it has not received recent reviews, which may indicate a lack of current user engagement or updates. This could be a concern for potential buyers looking for a vibrant user community.
- G2 reviewers highlight that Vertex AI's ease of use is a strong point, although it slightly trails behind Red Hat OpenShift Data Science in this area. Users appreciate the intuitive onboarding process, which helps them quickly adapt to the platform.
- Users report that both platforms offer robust features for model training and data ingestion, but Vertex AI's comprehensive approach to the ML lifecycle gives it an edge in user satisfaction. Reviewers specifically note how it reduces the effort needed to build and deploy models, making it a more efficient choice for many.
Pricing
Entry-Level Pricing
Gemini Enterprise Agent Platform
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
Red Hat OpenShift Data Science
No pricing availableFree Trial
Gemini Enterprise Agent Platform
No trial information availableRed Hat OpenShift Data Science
No trial information availableRatings
Meets Requirements
8.6
387
8.8
23
Ease of Use
8.1
398
8.5
23
Ease of Setup
8.1
320
Not enough data
Ease of Admin
7.9
150
Not enough data
Quality of Support
8.1
363
8.6
21
Has the product been a good partner in doing business?
8.3
144
Not enough data
Product Direction (% positive)
9.2
381
10.0
23
Features by Category
Deployment
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
Not enough data
Deployment
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
Not enough data
Management
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Not enough data
Operations
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Not enough data
Management
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Not enough data
Generative AI
8.4
37
Not enough data
8.6
37
Not enough data
System
8.2
170
8.9
22
Model Development
8.5
206
8.8
23
7.8
179
8.8
23
8.4
204
8.7
23
8.5
206
8.6
23
Model Development
8.2
164
8.8
23
Machine/Deep Learning Services
8.3
201
8.5
22
8.5
200
8.3
20
8.2
197
8.6
20
8.2
178
8.3
20
Machine/Deep Learning Services
8.5
164
8.6
20
8.5
163
8.7
21
Deployment
8.3
210
8.6
22
8.3
200
8.8
22
8.6
205
8.5
22
Generative AI
8.3
106
8.3
5
8.3
103
8.7
5
8.1
102
8.7
5
Agentic AI - Data Science and Machine Learning Platforms
8.0
35
Not enough data
7.8
34
Not enough data
7.6
36
Not enough data
7.8
32
Not enough data
8.4
34
Not enough data
7.4
33
Not enough data
7.6
33
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.0
31
Not enough data
8.7
32
Not enough data
8.6
31
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.0
34
Not enough data
7.7
31
Not enough data
8.1
30
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.5
30
Not enough data
8.3
32
Not enough data
8.5
31
Not enough data
Security and Compliance - Generative AI Infrastructure
8.7
30
Not enough data
8.3
32
Not enough data
8.9
30
Not enough data
Usability and Support - Generative AI Infrastructure
8.2
31
Not enough data
8.5
31
Not enough data
Integration - Machine Learning
8.5
66
Not enough data
Learning - Machine Learning
8.5
64
Not enough data
8.3
63
Not enough data
8.8
64
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
9.0
26
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.8
24
Not enough data
9.0
24
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.8
22
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
9.3
25
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
9.1
24
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
9.2
23
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
9.0
23
Not enough data
8.7
22
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
8.7
23
Not enough data
8.9
22
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.7
21
Not enough data
9.1
22
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.1
22
Not enough data
9.0
23
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
22
Not enough data
Customization - AI Agent Builders
8.6
28
Not enough data
7.6
27
Not enough data
8.3
26
Not enough data
Functionality - AI Agent Builders
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Not enough data
Data and Analytics - AI Agent Builders
7.8
26
Not enough data
7.9
27
Not enough data
8.1
28
Not enough data
Integration - AI Agent Builders
8.8
28
Not enough data
8.2
30
Not enough data
8.1
28
Not enough data
7.5
27
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
Gemini Enterprise Agent Platform and Red Hat OpenShift Data Science are categorized as Data Science and Machine Learning Platforms
Unique Categories
Gemini Enterprise Agent Platform is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Red Hat OpenShift Data Science has no unique categories
Reviews
Reviewers' Company Size
Gemini Enterprise Agent Platform
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
Computer Software
17.6%
Information Technology and Services
14.2%
Financial Services
6.9%
Retail
3.6%
Hospital & Health Care
3.4%
Other
54.2%
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
What is Google Cloud AI Platform used for?
3 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?
3 Comments
SA
Google Cloud ML Engine supports many software libraries, including TensorFlow, scikit-learn, XGBoost, Keras, etc....Read more
What is Google AI platform?
2 Comments
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
Red Hat OpenShift Data Science has no discussions with answers
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