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- IBM Decision Optimization
- Vertex AI
- IBM Decision Optimization-vs-Vertex AI
Compare IBM Decision Optimization and Vertex AI 
At a Glance
Market Segments
Enterprise (60.0% of reviews)
Small-Business (38.7% of reviews)
Entry-Level Pricing
Free
Pay As You Go Per Month
IBM Decision Optimization
Star Rating
(41)4.5 out of 5
Market Segments
Enterprise (60.0% of reviews)
Entry-Level Pricing
Free
Browse all 2 pricing plansVertex AI
Star Rating
(572)4.3 out of 5
Market Segments
Small-Business (38.7% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
Free Trial is available
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Vertex AI excels in AI High Availability with a score of 9.2, which reviewers mention significantly enhances their operational reliability compared to IBM Decision Optimization's lower performance in this area.
- Reviewers mention that Vertex AI's AI Model Training Scalability, rated at 8.5, allows for more efficient handling of large datasets, while IBM Decision Optimization's score of 7.4 indicates challenges in scaling model training effectively.
- G2 users highlight that Vertex AI's integration capabilities, particularly with AI Data Pipeline Integration rated at 8.2, provide a smoother workflow, whereas IBM Decision Optimization's integration features are perceived as less flexible, scoring 7.2 in Data Ingestion & Wrangling.
- Users on G2 report that Vertex AI's pre-built algorithms, rated at 8.4, offer a robust starting point for model development, while IBM Decision Optimization's score of 8.0 suggests it may not provide as comprehensive a library for users looking to jumpstart their projects.
- Reviewers mention that Vertex AI's AI Cost per API Call, rated at 8.0, is more favorable for budget-conscious users, while IBM Decision Optimization's lower score of 7.9 indicates potential cost concerns for users scaling their operations.
- Users say that Vertex AI's documentation quality, rated at 8.5, is superior, making it easier for teams to onboard and utilize the platform effectively, while IBM Decision Optimization's documentation, although decent, does not match the clarity and comprehensiveness of Vertex AI's resources.
Pricing
Entry-Level Pricing
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
Vertex AI
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
Free Trial
IBM Decision Optimization
Free Trial is available
Vertex AI
Free Trial is available
Ratings
Meets Requirements
8.4
30
8.6
346
Ease of Use
8.7
30
8.3
353
Ease of Setup
8.3
14
8.2
272
Ease of Admin
8.7
14
8.0
143
Quality of Support
8.6
30
8.2
325
Has the product been a good partner in doing business?
9.2
14
8.2
137
Product Direction (% positive)
8.0
29
9.2
338
Features by Category
Deployment
Not enough data
8.4
64
Not enough data
8.2
65
Not enough data
8.4
65
Not enough data
8.4
61
Not enough data
8.8
61
Deployment
Not enough data
8.5
65
Not enough data
8.3
64
Not enough data
8.4
63
Not enough data
8.5
63
Not enough data
8.8
61
Management
Not enough data
8.4
61
Not enough data
8.5
60
Not enough data
8.1
61
Not enough data
8.2
61
Operations
Not enough data
8.3
61
Not enough data
8.5
62
Not enough data
8.5
62
Management
Not enough data
8.3
60
Not enough data
8.5
61
Not enough data
8.4
60
Generative AI
Not enough data
8.1
26
Not enough data
8.5
26
System
7.2
9
8.3
136
8.0
9
8.2
138
7.8
9
7.9
133
Model Development
7.9
7
8.4
165
8.3
7
7.9
142
8.3
7
8.5
163
7.4
7
8.5
165
Model Development
8.0
9
8.4
131
7.8
9
8.5
133
7.4
9
8.4
131
Machine/Deep Learning Services
7.9
7
8.3
163
7.4
7
8.5
159
6.9
7
8.3
158
6.9
7
8.3
141
Machine/Deep Learning Services
7.7
8
8.4
134
7.3
8
8.5
131
8.1
8
8.3
129
7.5
8
8.4
130
Deployment
8.3
7
8.3
157
8.1
7
8.3
158
7.6
7
8.6
157
Deployment
8.3
7
8.3
129
8.3
7
8.3
130
8.8
7
8.4
131
Generative AI
Not enough data
8.2
67
Not enough data
8.5
67
Not enough data
8.2
68
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
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
Not enough data
9.2
18
Not enough data
8.7
18
Not enough data
8.7
18
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.1
18
Not enough data
8.1
18
Not enough data
8.4
18
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.6
18
Not enough data
8.2
18
Not enough data
8.7
18
Security and Compliance - Generative AI Infrastructure
Not enough data
8.8
18
Not enough data
8.4
18
Not enough data
9.3
18
Usability and Support - Generative AI Infrastructure
Not enough data
8.5
18
Not enough data
8.4
18
Integration - Machine Learning
Not enough data
8.6
52
Learning - Machine Learning
Not enough data
8.6
51
Not enough data
8.4
50
Not enough data
8.8
51
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
8.9
16
Not enough data
8.8
16
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
16
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
16
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
16
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
16
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
16
Not enough data
8.4
16
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
15
Not enough data
9.0
15
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
15
Not enough data
9.2
15
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
16
Not enough data
8.8
16
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
16
Customization - AI Agent Builders
Not enough data
8.5
17
Not enough data
7.5
17
Not enough data
8.2
16
Functionality - AI Agent Builders
Not enough data
7.9
17
Not enough data
6.8
17
Not enough data
8.3
16
Not enough data
6.8
17
Data and Analytics - AI Agent Builders
Not enough data
7.6
16
Not enough data
7.8
17
Not enough data
7.9
17
Integration - AI Agent Builders
Not enough data
8.8
17
Not enough data
7.8
17
Not enough data
8.1
17
Not enough data
7.5
17
Categories
Categories
Shared Categories
IBM Decision Optimization and Vertex AI are categorized as Data Science and Machine Learning Platforms
Unique Categories
IBM Decision Optimization is categorized as Decision Management Platforms
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, and AI Agent Builders
Reviews
Reviewers' Company Size
IBM Decision Optimization
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Vertex 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
Financial Services
14.3%
Education Management
11.4%
Computer Software
11.4%
Transportation/Trucking/Railroad
5.7%
Research
5.7%
Other
51.4%
Computer Software
16.4%
Information Technology and Services
14.3%
Financial Services
6.7%
Retail
4.0%
Consulting
3.7%
Other
54.9%
Most Helpful Reviews
IBM Decision Optimization
Most Helpful Favorable Review
Deepak R.
Verified User in Financial Services
The best thing I like about IBM Decision Optimization is its capability to provide prescriptive analytics.
Most Helpful Critical Review
Verified User in Banking
Is hard to follow the results of the Decisions, as well, there is no control of them and if in some point a decision is allowing to pass to many results there is no an alarm or control.
Vertex AI
Most Helpful Favorable Review
Rehan T.
Verified User in Design
Vertex AI is great because it's all-in-one. You can do everything for machine learning in one place, which makes it easy. It can handle big tasks without any problems. It also helps a lot by doing some of the hard work for you, like building models. And it...
Most Helpful Critical Review
Verified User in Oil & Energy
the lack of documentation. you have to guess the concept of operation, constantly go to the help in the command line to explore what is available..... the packaging of your code is annoyingh to do the first two times since you have to guess a lot by...
Deepak R.
Verified User in Financial Services
The best thing I like about IBM Decision Optimization is its capability to provide prescriptive analytics.
Rehan T.
Verified User in Design
Vertex AI is great because it's all-in-one. You can do everything for machine learning in one place, which makes it easy. It can handle big tasks without any problems. It also helps a lot by doing some of the hard work for you, like building models. And it...
Verified User in Banking
Is hard to follow the results of the Decisions, as well, there is no control of them and if in some point a decision is allowing to pass to many results there is no an alarm or control.
Verified User in Oil & Energy
the lack of documentation. you have to guess the concept of operation, constantly go to the help in the command line to explore what is available..... the packaging of your code is annoyingh to do the first two times since you have to guess a lot by...
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Discussions
IBM Decision Optimization has no discussions with answers
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