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Compare Amazon SageMaker and ClearML 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Small-Business (53.8% of reviews)
Entry-Level Pricing
No pricing available
Free 1 seat
Amazon SageMaker
Star Rating
(46)4.2 out of 5
Market Segments
Small-Business (33.3% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Amazon SageMakerClearML
Star Rating
(13)4.7 out of 5
Market Segments
Small-Business (53.8% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
Free 1 seat
Learn more about ClearMLSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its scalability, with a perfect score of 10.0, allowing businesses to efficiently handle large datasets and complex models. In contrast, ClearML, while still strong, has a slightly lower scalability score of 9.7, which may impact users with rapidly growing data needs.
- Reviewers mention that ClearML offers a free entry-level price, making it an attractive option for small businesses and startups looking to experiment with machine learning without financial commitment. Amazon SageMaker, on the other hand, does not provide a free tier, which could be a barrier for budget-conscious users.
- G2 users highlight that ClearML has a higher G2 rating of 4.7 compared to Amazon SageMaker's 4.2, indicating a stronger overall user satisfaction and potentially better user experience in day-to-day operations.
- Users on G2 report that Amazon SageMaker's quality of support is rated at 8.7, while ClearML shines with a score of 9.0, suggesting that users may find more responsive and helpful support when using ClearML.
- Reviewers mention that both platforms have strong model training capabilities, with Amazon SageMaker scoring 8.9 and ClearML matching that score. However, ClearML's additional features in feature engineering (8.6) may provide users with more tools for optimizing their models.
- Users say that Amazon SageMaker's deployment ease is rated at 9.7, which is higher than ClearML's 9.0, indicating that users may find it easier to deploy their models on SageMaker, potentially leading to faster time-to-market for their applications.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableFree Trial
Amazon SageMaker
No trial information availableClearML
Free Trial is available
Ratings
Meets Requirements
8.6
37
9.1
11
Ease of Use
8.4
38
8.9
11
Ease of Setup
8.5
25
Not enough data
Ease of Admin
8.4
20
Not enough data
Quality of Support
8.6
33
9.0
10
Has the product been a good partner in doing business?
9.2
20
Not enough data
Product Direction (% positive)
9.0
36
10.0
11
Features by Category
Deployment
Not enough data
10.0
5
Not enough data
9.2
6
Not enough data
10.0
6
Not enough data
9.7
5
Not enough data
10.0
6
Deployment
Not enough data
9.2
6
Not enough data
8.8
8
Not enough data
9.4
8
Not enough data
9.0
7
Not enough data
9.8
8
Management
Not enough data
8.9
6
Not enough data
8.3
6
Not enough data
9.3
5
Not enough data
9.5
7
Operations
Not enough data
8.8
7
Not enough data
8.6
7
Not enough data
9.4
8
Management
Not enough data
9.3
7
Not enough data
8.5
8
Not enough data
9.2
6
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.2
19
Not enough data
Model Development
8.7
29
Not enough data
8.2
28
Not enough data
8.3
33
Not enough data
8.9
33
Not enough data
Model Development
8.4
19
Not enough data
Machine/Deep Learning Services
8.9
26
Not enough data
9.1
28
Not enough data
8.9
25
Not enough data
9.0
28
Not enough data
Machine/Deep Learning Services
9.2
17
Not enough data
9.2
18
Not enough data
Deployment
8.6
32
Not enough data
8.6
32
Not enough data
9.0
31
Not enough data
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
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 ClearML are categorized as MLOps Platforms
Unique Categories
Amazon SageMaker is categorized as Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
ClearML is categorized as Other Development
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
ClearML
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
38.5%
Computer Software
38.5%
Media Production
7.7%
Hospital & Health Care
7.7%
Consumer Goods
7.7%
Other
0.0%
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
ClearML has no discussions with answers
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