Compare Gemini Enterprise Agent Platform and IBM Decision Optimization 
At a Glance
Market Segments
Small-Business (42.2% of reviews)
Enterprise (60.0% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
Free
Gemini Enterprise Agent Platform
Star Rating
(651)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 PlatformIBM Decision Optimization
Star Rating
(41)4.5 out of 5
Market Segments
Enterprise (60.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
Free
Browse all 2 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- According to verified reviews, Vertex AI excels in managing complex machine learning workflows, with users praising its ability to centralize the entire ML lifecycle, from data preparation to deployment and monitoring. This integration significantly reduces the effort needed to build, train, and deploy models.
- G2 reviewers report that IBM Decision Optimization is user-friendly and benefits from a strong community of experts, which fosters communication and learning opportunities. Users appreciate the sophisticated algorithms it offers for tackling complex decision-making scenarios, making it suitable for intricate problems.
- Users say that Vertex AI has a higher overall satisfaction rating, reflecting its robust features and seamless integration with Google Cloud. This integration allows for a more streamlined experience, which is particularly beneficial for small businesses looking to leverage machine learning.
- Reviewers mention that while IBM Decision Optimization has a solid ease of use, it faces challenges in scalability compared to Vertex AI. Users have noted that it can handle large datasets efficiently, but may not scale as effectively for growing business needs.
- According to recent feedback, Vertex AI shines in its model training capabilities, with users highlighting its effectiveness in feature engineering and natural language processing. This makes it a strong choice for businesses focused on advanced machine learning applications.
- G2 reviewers indicate that IBM Decision Optimization offers a free entry-level price, which can be appealing for enterprises looking to explore decision optimization without initial costs. However, the limited number of recent reviews suggests that it may not be as actively supported or updated as Vertex AI.
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
IBM Decision Optimization
IBM ILOG CPLEX Optimization Studio Free edition
Free
Explore and learn optimization and constraint programming modeling with our free edition. Try free edition:
https://www.ibm.com/account/reg/signup?formid=urx-20028
- Limited to 1000 variables and 1000 constraints, but models can be edited
- Use both the OPL/IDE and API modeling/solving
Free Trial
Gemini Enterprise Agent Platform
No trial information availableIBM Decision Optimization
Free Trial is available
Ratings
Meets Requirements
8.6
389
8.4
30
Ease of Use
8.1
400
8.7
30
Ease of Setup
8.1
322
8.3
14
Ease of Admin
7.9
150
8.7
14
Quality of Support
8.1
365
8.6
30
Has the product been a good partner in doing business?
8.3
144
9.2
14
Product Direction (% positive)
9.2
383
8.0
29
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
173
7.2
9
Model Development
8.5
209
7.9
7
7.9
182
8.3
7
8.5
207
8.3
7
8.5
209
7.4
7
Model Development
8.2
167
7.4
9
Machine/Deep Learning Services
8.3
204
7.9
7
8.5
203
7.4
7
8.2
200
6.9
7
8.3
181
6.9
7
Machine/Deep Learning Services
8.5
167
7.3
8
8.5
166
7.5
8
Deployment
8.3
212
8.3
7
8.3
203
8.1
7
8.6
207
7.6
7
Generative AI
8.3
109
Not enough data
8.3
106
Not enough data
8.1
105
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
8.1
38
Not enough data
7.8
37
Not enough data
7.7
38
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Not enough data
Integration
Not enough data
Not enough data
Not enough data
Not enough data
Business Logic
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
Analytics
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
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 - Decision Management 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
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
68
Not enough data
Learning - Machine Learning
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
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 IBM Decision Optimization 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
IBM Decision Optimization is categorized as Decision Management Platforms
Reviews
Reviewers' Company Size
Gemini Enterprise Agent Platform
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Decision Optimization
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
17.9%
Information Technology and Services
14.3%
Financial Services
6.8%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
Financial Services
14.3%
Computer Software
11.4%
Education Management
11.4%
Transportation/Trucking/Railroad
5.7%
Research
5.7%
Other
51.4%
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
IBM Decision Optimization has no discussions with answers
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
Your software and services insights are valuable.
Your peers come to G2 to get an inside look at and other business solutions. Adding perspective on will help others pick the right solution based on real user experience.
