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Compare Amazon SageMaker and IBM Watson Studio 
Featured Products
At a Glance
Market Segments
Enterprise (34.9% of reviews)
Enterprise (50.9% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Amazon SageMaker
Star Rating
(48)4.2 out of 5
Market Segments
Enterprise (34.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerIBM Watson Studio
Star Rating
(166)4.2 out of 5
Market Segments
Enterprise (50.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioAmazon SageMaker vs IBM Watson Studio
When assessing the two solutions, reviewers found Amazon SageMaker easier to use, set up, and administer. Reviewers also preferred doing business with Amazon SageMaker overall.
- Reviewers felt that Amazon SageMaker meets the needs of their business better than IBM Watson Studio.
- When comparing quality of ongoing product support, reviewers felt that Amazon SageMaker is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Amazon SageMaker over IBM Watson Studio.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableIBM Watson Studio
No pricing availableFree Trial
Amazon SageMaker
No trial information availableIBM Watson Studio
No trial information availableRatings
Meets Requirements
8.6
38
8.3
122
Ease of Use
8.4
39
8.0
123
Ease of Setup
8.5
26
7.6
101
Ease of Admin
8.4
20
7.8
95
Quality of Support
8.6
34
8.2
114
Has the product been a good partner in doing business?
9.2
20
8.0
94
Product Direction (% positive)
9.1
37
8.5
116
Features by Category
Data Source Access
Not enough data
9.0
13
Not enough data
9.3
12
Not enough data
9.2
14
Data Interaction
Not enough data
9.0
14
Not enough data
9.2
12
Not enough data
9.4
12
Not enough data
9.1
13
Not enough data
9.2
12
Not enough data
9.2
13
Not enough data
9.1
13
Not enough data
9.6
12
Data Exporting
Not enough data
9.4
12
Not enough data
9.2
12
Not enough data
9.2
12
Generative AI
Not enough data
Not enough data
Deployment
Not enough data
8.8
8
Not enough data
9.2
8
Not enough data
9.0
8
Not enough data
9.4
8
Not enough data
8.8
8
Deployment
Not enough data
9.0
8
Not enough data
8.8
8
Not enough data
8.8
8
Not enough data
9.4
8
Not enough data
9.2
8
Management
Not enough data
9.3
7
Not enough data
9.6
8
Not enough data
9.0
7
Not enough data
9.0
8
Operations
Not enough data
9.0
8
Not enough data
9.0
8
Not enough data
9.3
7
Management
Not enough data
9.5
7
Not enough data
9.4
8
Not enough data
8.8
7
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.3
20
9.1
13
Model Development
8.7
29
8.6
34
8.2
28
8.9
35
8.3
33
8.5
36
8.9
33
8.4
37
Model Development
8.4
19
9.4
13
Machine/Deep Learning Services
8.9
26
8.6
28
9.1
28
8.5
35
8.9
25
Feature Not Available
9.0
28
8.6
28
Machine/Deep Learning Services
9.2
17
8.9
12
9.2
18
9.0
12
Deployment
8.7
33
8.5
32
8.7
33
8.6
33
9.0
31
8.7
31
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
Setup
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
9.7
6
Data
Not enough data
8.6
7
Not enough data
8.6
7
Analysis
Not enough data
9.7
6
Not enough data
8.1
7
Not enough data
8.1
7
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.1
7
Not enough data
7.9
7
Customization
Not enough data
9.0
7
Not enough data
8.1
7
Not enough data
9.2
6
Generative AI
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
Statistical Tool
Not enough data
8.0
14
Not enough data
8.4
15
Not enough data
8.1
15
Data Analysis
Not enough data
8.7
15
Not enough data
9.0
14
Decision Making
Not enough data
8.6
14
Not enough data
8.6
15
Not enough data
8.3
13
Not enough data
8.7
14
Generative AI
Not enough data
9.3
5
Not enough data
8.3
5
Categories
Categories
Shared Categories
Amazon SageMaker and IBM Watson Studio are categorized as MLOps Platforms and Data Science and Machine Learning Platforms
Unique Categories
Amazon SageMaker is categorized as Generative AI Infrastructure and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Watson Studio
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
20.9%
Computer Software
16.3%
Marketing and Advertising
4.7%
Internet
4.7%
Hospital & Health Care
4.7%
Other
48.8%
Information Technology and Services
15.7%
Computer Software
13.2%
Telecommunications
8.2%
Banking
7.5%
Education Management
5.7%
Other
49.7%
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 Watson Studio has no discussions with answers
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