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Compare Azure Machine Learning and Microsoft Fabric 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (58.8% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure 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 LearningMicrosoft Fabric
Star Rating
(17)4.6 out of 5
Market Segments
Enterprise (58.8% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Microsoft FabricSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in Model Training with a score of 8.7, highlighting its robust capabilities for training complex models, while Microsoft Fabric received a slightly higher score of 8.8, indicating a more user-friendly experience in this area.
- Reviewers mention that Azure Machine Learning offers strong Data Ingestion & Wrangling features with a score of 8.7, but Microsoft Fabric shines with a higher score of 9.1, showcasing its superior flexibility and ease of use in handling diverse data sources.
- G2 users indicate that Azure Machine Learning's Quality of Support is commendable with a score of 8.6, but Microsoft Fabric outperforms it with a score of 9.0, suggesting that users find the support team more responsive and helpful.
- Users on G2 report that Azure Machine Learning has a solid Scalability rating of 9.0, which is impressive, but Microsoft Fabric takes the lead with a score of 9.2, indicating better performance in scaling applications to meet growing demands.
- Reviewers mention that Azure Machine Learning's Ease of Deployment is rated at 8.7, which is good, but Microsoft Fabric's score of 9.0 suggests a more streamlined deployment process, making it easier for users to get started quickly.
- Users say that Azure Machine Learning's Language Support is rated at 8.9, which is favorable, but Microsoft Fabric's score of 9.1 indicates a broader range of supported languages, enhancing its versatility for diverse user needs.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableMicrosoft Fabric
No pricing availableFree Trial
Azure Machine Learning
No trial information availableMicrosoft Fabric
No trial information availableRatings
Meets Requirements
8.5
81
9.3
17
Ease of Use
8.5
80
8.9
17
Ease of Setup
8.3
57
8.9
6
Ease of Admin
8.3
49
Not enough data
Quality of Support
8.6
74
9.0
17
Has the product been a good partner in doing business?
8.6
47
Not enough data
Product Direction (% positive)
9.0
80
10.0
17
Features by Category
Deployment
Not enough data
8.8
15
Not enough data
8.8
15
Not enough data
8.7
15
Not enough data
8.8
15
Not enough data
9.1
15
Deployment
Not enough data
8.9
15
Not enough data
9.2
15
Not enough data
9.1
15
Not enough data
9.1
15
Not enough data
9.2
15
Management
Not enough data
8.7
14
Not enough data
8.8
15
Not enough data
8.9
14
Not enough data
8.9
14
Operations
Not enough data
8.9
15
Not enough data
8.9
15
Not enough data
9.0
15
Management
Not enough data
8.6
15
Not enough data
8.6
14
Not enough data
8.6
14
Generative AI
Not enough data
7.9
13
Not enough data
8.1
13
System
8.6
22
Not enough data
Model Development
8.6
51
Not enough data
8.9
54
Not enough data
8.3
53
Not enough data
8.7
52
Not enough data
Model Development
8.4
21
Not enough data
Machine/Deep Learning Services
8.1
45
Not enough data
7.9
45
Not enough data
7.8
38
Not enough data
8.2
42
Not enough data
Machine/Deep Learning Services
8.7
21
Not enough data
8.5
21
Not enough data
Deployment
8.8
50
Not enough data
8.7
51
Not enough data
8.9
51
Not enough data
Generative AI
8.5
10
Not enough data
8.2
10
Not enough data
7.5
10
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
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
Azure Machine Learning and Microsoft Fabric are categorized as MLOps Platforms
Unique Categories
Azure Machine Learning is categorized as Large Language Model Operationalization (LLMOps), Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Microsoft Fabric has no unique categories
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Microsoft Fabric
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
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
Education Management
17.6%
Manufacturing
11.8%
Information Technology and Services
11.8%
Electrical/Electronic Manufacturing
11.8%
Consulting
5.9%
Other
41.2%
Alternatives
Discussions
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
Microsoft Fabric has no discussions with answers
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