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Compare Amazon SageMaker and Azure Machine Learning 
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At a Glance
Market Segments
Small-Business (33.3% of reviews)
Enterprise (38.8% 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 SageMakerAzure Machine Learning
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (38.8% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Amazon SageMaker excels in its Scalability with a score of 9.0, allowing for seamless handling of large datasets and model training, while Azure Machine Learning, with a score of 8.9, is noted for its solid performance but may not scale as efficiently in high-demand scenarios.
- Reviewers mention that SageMaker's Drag and Drop feature scores 9.0, making it user-friendly for those who prefer a visual interface, whereas Azure's score of 8.7 indicates it is less intuitive, which may hinder users who are not as technically inclined.
- G2 users highlight that Amazon SageMaker's Model Training capabilities score 8.9, with reviewers praising its pre-built algorithms and ease of use, while Azure Machine Learning, scoring 8.7, is seen as slightly less effective in this area, particularly for users looking for quick deployment of models.
- Users on G2 report that Azure Machine Learning shines in Natural Language Processing with a score of 7.9, but SageMaker outperforms it in Natural Language Generation with a score of 8.8, indicating a stronger capability for generating human-like text.
- Reviewers mention that SageMaker's Quality of Support is rated at 8.7, with many users appreciating the responsiveness and helpfulness of the support team, while Azure's score of 8.6 suggests a slightly less favorable experience, particularly in resolving complex issues.
- Users say that both platforms offer Managed Service capabilities, but SageMaker's higher score of 9.5 indicates a more robust and reliable managed service experience compared to Azure's 8.8, which may lead to better overall user satisfaction.
Pricing
Entry-Level Pricing
Amazon SageMaker
No pricing availableAzure Machine Learning
No pricing availableFree Trial
Amazon SageMaker
No trial information availableAzure Machine Learning
No trial information availableRatings
Meets Requirements
8.6
37
8.5
81
Ease of Use
8.4
38
8.5
80
Ease of Setup
8.5
25
8.3
57
Ease of Admin
8.4
20
8.3
49
Quality of Support
8.6
33
8.6
74
Has the product been a good partner in doing business?
9.2
20
8.6
47
Product Direction (% positive)
9.0
36
9.0
80
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
8.6
22
Model Development
8.7
29
8.6
51
8.2
28
8.9
54
8.3
33
8.3
53
8.9
33
8.7
52
Model Development
8.4
19
8.4
21
Machine/Deep Learning Services
8.9
26
8.1
45
9.1
28
7.9
45
8.9
25
7.8
38
9.0
28
8.2
42
Machine/Deep Learning Services
9.2
17
8.7
21
9.2
18
8.5
21
Deployment
8.6
32
8.8
50
8.6
32
8.7
51
9.0
31
8.9
51
Generative AI
8.6
6
8.5
10
9.2
6
8.2
10
8.3
5
7.5
10
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
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
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
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 Azure Machine Learning are categorized as MLOps Platforms, Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Unique Categories
Amazon SageMaker has no unique categories
Azure Machine Learning is categorized as Large Language Model Operationalization (LLMOps)
Reviews
Reviewers' Company Size
Amazon SageMaker
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Azure Machine Learning
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
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
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
What is Azure Machine Learning Studio used for?
1 Comment
AR
In short, to build, deploy, and manage high-quality models faster and with confidence.Read more
Azure Machine Learning has no more discussions with answers
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