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Compare Amazon SageMaker and IBM Decision Optimization 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Enterprise (60.0% of reviews)
Entry-Level Pricing
No pricing available
Free
Amazon SageMaker
Star Rating
(45)4.3 out of 5
Market Segments
Small-Business (33.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerIBM 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 plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its Drag and Drop feature, with a score of 9.0, making it easier for users to build and deploy machine learning models without extensive coding knowledge. In contrast, IBM Decision Optimization's Drag and Drop feature scored lower at 7.8, indicating a less user-friendly experience.
- Reviewers mention that Amazon SageMaker's Model Training capabilities are highly rated at 8.9, showcasing its efficiency in training complex models. Conversely, IBM Decision Optimization received a score of 7.4, suggesting that users may face challenges in this area.
- G2 users highlight Amazon SageMaker's superior Scalability with a score of 9.7, which allows businesses to easily scale their machine learning applications. IBM Decision Optimization, however, scored 8.8, indicating that while it is scalable, it may not match the flexibility offered by SageMaker.
- Users on G2 report that Amazon SageMaker provides better Language Support with a score of 8.9, which is crucial for diverse teams working on various projects. IBM Decision Optimization's score of 7.9 suggests it may have limitations in this area.
- Reviewers mention that Amazon SageMaker's AI High Availability features are robust, contributing to its high performance and reliability, while IBM Decision Optimization's lower score of 7.6 indicates potential concerns regarding uptime and reliability.
- Users say that Amazon SageMaker's Pre-Built Algorithms are well-received, scoring 8.4, which helps users quickly implement solutions. In contrast, IBM Decision Optimization's score of 8.0 suggests that while it offers pre-built algorithms, they may not be as comprehensive or effective as those in SageMaker.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableIBM 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
Amazon SageMaker
No trial information availableIBM Decision Optimization
Free Trial is available
Ratings
Meets Requirements
8.6
37
8.4
30
Ease of Use
8.4
38
8.7
30
Ease of Setup
8.5
25
8.3
14
Ease of Admin
8.4
20
8.7
14
Quality of Support
8.6
33
8.6
30
Has the product been a good partner in doing business?
9.2
20
9.2
14
Product Direction (% positive)
9.0
36
8.0
29
Features by Category
Deployment
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
Deployment
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
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
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
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.2
19
7.2
9
Model Development
8.7
29
7.9
7
8.2
28
8.3
7
8.3
33
8.3
7
8.9
33
7.4
7
Model Development
8.4
19
7.4
9
Machine/Deep Learning Services
8.9
26
7.9
7
9.1
28
7.4
7
8.9
25
6.9
7
9.0
28
6.9
7
Machine/Deep Learning Services
9.2
17
7.3
8
9.2
18
7.5
8
Deployment
8.6
32
8.3
7
8.6
32
8.1
7
9.0
31
7.6
7
Generative AI
8.6
6
Not enough data
9.2
6
Not enough data
8.3
5
Not enough data
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
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
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
Amazon SageMaker and IBM Decision Optimization are categorized as Data Science and Machine Learning Platforms
Unique Categories
Amazon SageMaker is categorized as MLOps Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
IBM Decision Optimization is categorized as Decision Management Platforms
Reviews
Reviewers' Company Size
Amazon SageMaker
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
Information Technology and Services
19.0%
Computer Software
16.7%
Marketing and Advertising
4.8%
Internet
4.8%
Hospital & Health Care
4.8%
Other
50.0%
Financial Services
14.3%
Education Management
11.4%
Computer Software
11.4%
Transportation/Trucking/Railroad
5.7%
Research
5.7%
Other
51.4%
Alternatives
Discussions
What is the best way to integrate Sagemaker models with Kubernetes?
1 Comment
VJ
https://aws.amazon.com/blogs/machine-learning/introducing-amazon-sagemaker-operators-for-kubernetes/Read more
How do i make this platform reach to most of my developers?
1 Comment
VJ
you can manage the access via IAM users and roles and give them access as per their need, Sagemaker by default has all the basic AWS feature and you can...Read more
Amazon SageMaker has no more discussions with answers
IBM Decision Optimization has no discussions with answers
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