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Compare Amazon SageMaker and Azure Machine Learning 
Featured Products
At a Glance
Market Segments
Enterprise (34.9% of reviews)
Enterprise (38.8% 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 SageMakerAzure Machine Learning
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (38.8% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningAmazon SageMaker vs Azure Machine Learning
When assessing the two solutions, reviewers found Azure Machine Learning easier to use. However, Amazon SageMaker is easier to 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 Azure Machine Learning.
- When comparing quality of ongoing product support, Amazon SageMaker and Azure Machine Learning provide similar levels of assistance.
- For feature updates and roadmaps, our reviewers preferred the direction of Amazon SageMaker over Azure Machine Learning.
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
38
8.5
81
Ease of Use
8.4
39
8.5
80
Ease of Setup
8.5
26
8.3
57
Ease of Admin
8.4
20
8.3
49
Quality of Support
8.6
34
8.6
74
Has the product been a good partner in doing business?
9.2
20
8.6
47
Product Direction (% positive)
9.1
37
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
8.3
20
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
8.7
33
8.8
50
8.7
33
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
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
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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