Compare Azure Machine Learning and Microsoft Fabric 
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (39.5% 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
(39)4.7 out of 5
Market Segments
Enterprise (39.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Microsoft FabricAzure Machine Learning vs Microsoft Fabric
When assessing the two solutions, reviewers found Microsoft Fabric easier to use, set up, and administer. Reviewers also preferred doing business with Microsoft Fabric overall.
- Reviewers felt that Microsoft Fabric meets the needs of their business better than Azure Machine Learning.
- When comparing quality of ongoing product support, reviewers felt that Microsoft Fabric is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Microsoft Fabric over Azure Machine Learning.
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
9.4
33
Ease of Use
8.5
80
9.1
34
Ease of Setup
8.3
57
9.2
17
Ease of Admin
8.3
49
9.0
8
Quality of Support
8.6
74
9.2
33
Has the product been a good partner in doing business?
8.6
47
9.8
8
Product Direction (% positive)
9.0
80
10.0
33
Features by Category
Deployment
Not enough data
9.1
20
Not enough data
9.0
18
Not enough data
8.8
18
Not enough data
8.9
18
Not enough data
9.3
19
Deployment
Not enough data
9.0
17
Not enough data
9.3
17
Not enough data
9.3
18
Not enough data
9.2
17
Not enough data
9.3
16
Management
Not enough data
8.9
16
Not enough data
8.9
17
Not enough data
9.1
16
Not enough data
9.1
16
Operations
Not enough data
9.0
16
Not enough data
9.0
17
Not enough data
9.2
18
Management
Not enough data
8.6
16
Not enough data
8.7
15
Not enough data
8.7
15
Generative AI
Not enough data
8.1
14
Not enough data
8.2
14
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%
Insurance
15.8%
Information Technology and Services
15.8%
Manufacturing
7.9%
Education Management
7.9%
Commercial Real Estate
5.3%
Other
47.4%
Discussions
What is Azure Machine Learning Studio used for?
2 Comments
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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