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Compare Amazon SageMaker and IBM Watson Studio 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Enterprise (51.3% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
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 Watson Studio
Star Rating
(164)4.2 out of 5
Market Segments
Enterprise (51.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in "Ease of Deployment" with a score of 9.8, making it particularly user-friendly for teams looking to quickly implement machine learning models. In contrast, IBM Watson Studio, while still strong, has a slightly lower score of 9.5 in this area, indicating that some users may find it a bit more complex to deploy their models.
- Reviewers mention that Amazon SageMaker offers superior "Scalability" with a score of 9.0, which is crucial for businesses anticipating growth. IBM Watson Studio also performs well with a score of 9.2, but users have noted that SageMaker's scalability features are more robust for larger datasets and workloads.
- G2 users highlight the "Quality of Support" as a strong point for Amazon SageMaker, scoring 8.7, with many reviewers appreciating the responsiveness and helpfulness of the support team. Conversely, IBM Watson Studio has a lower score of 8.2, with some users expressing concerns about the timeliness of support responses.
- Users on G2 report that Amazon SageMaker's "Breadth of Data Sources" is impressive, scoring 8.9, which allows for seamless integration with various data sources. IBM Watson Studio, while also strong, has not received as high a score in this area, indicating that users may find fewer options for data connectivity.
- Reviewers mention that Amazon SageMaker shines in "Model Training" with a score of 8.9, where users appreciate the platform's efficiency and effectiveness in training models. IBM Watson Studio, with a score of 9.0, is competitive but users have noted that SageMaker's training capabilities are often more intuitive and faster.
- Users say that the "No-Code" feature in Amazon SageMaker, scoring 9.7, is a game-changer for non-technical users, allowing them to build models without extensive coding knowledge. IBM Watson Studio also offers no-code options but scores slightly lower at 9.2, suggesting that SageMaker may provide a more accessible experience for those less familiar with programming.
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
37
8.3
121
Ease of Use
8.4
38
8.0
122
Ease of Setup
8.5
25
7.6
100
Ease of Admin
8.4
20
7.8
95
Quality of Support
8.6
33
8.2
113
Has the product been a good partner in doing business?
9.2
20
8.0
94
Product Direction (% positive)
9.0
36
8.5
115
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
8.2
19
9.0
12
Model Development
8.7
29
8.5
33
8.2
28
8.8
34
8.3
33
8.5
35
8.9
33
8.3
36
Model Development
8.4
19
9.4
13
Machine/Deep Learning Services
8.9
26
8.5
27
9.1
28
8.5
34
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
8.6
32
8.5
32
8.6
32
8.6
33
9.0
31
8.6
30
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
19.0%
Computer Software
16.7%
Marketing and Advertising
4.8%
Internet
4.8%
Hospital & Health Care
4.8%
Other
50.0%
Information Technology and Services
15.8%
Computer Software
13.3%
Telecommunications
8.2%
Banking
7.6%
Education Management
5.7%
Other
49.4%
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 Watson Studio has no discussions with answers
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