Compare Microsoft Fabric and SAS Viya 
At a Glance
Market Segments
Enterprise (40.0% of reviews)
Enterprise (34.2% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Microsoft Fabric
Star Rating
(41)4.7 out of 5
Market Segments
Enterprise (40.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Microsoft FabricSAS Viya
Star Rating
(795)4.3 out of 5
Market Segments
Enterprise (34.2% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about SAS ViyaSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that SAS Viya excels in its cloud-native architecture, which allows for seamless integration of data preparation, advanced analytics, and machine learning within a single platform. Users appreciate the flexibility and accessibility it offers, enabling them to work on projects from various locations.
- Users say Microsoft Fabric stands out for its unified platform that combines data engineering, ETL, analytics, and visualization. Reviewers highlight how the integration of services like Data Factory and Power BI reduces data movement and enhances overall efficiency, making it a comprehensive solution for business intelligence needs.
- According to verified reviews, SAS Viya's user interface is praised for being user-friendly, which helps users navigate the platform easily. This is particularly beneficial for those who may not have extensive technical backgrounds, as it simplifies complex tasks like keyword and sentiment analysis.
- Reviewers mention that Microsoft Fabric offers a high level of feature richness, with many tools available in one place. This integration is seen as a significant advantage, as it allows users to manage their data projects without needing to switch between multiple applications.
- G2 reviewers highlight that while SAS Viya has a strong presence in the small business market, it may not fully meet the needs of larger enterprises as effectively as Microsoft Fabric, which is favored by enterprise users for its scalability and robust support.
- Users report that Microsoft Fabric's support quality is notably high, with many praising the responsiveness and helpfulness of the support team. In contrast, while SAS Viya also receives positive feedback for its support, it does not match the level of satisfaction reported by Microsoft Fabric users.
Pricing
Entry-Level Pricing
Microsoft Fabric
No pricing availableSAS Viya
No pricing availableFree Trial
Microsoft Fabric
No trial information availableSAS Viya
Free Trial is available
Ratings
Meets Requirements
9.4
35
8.5
546
Ease of Use
9.1
36
8.2
630
Ease of Setup
9.1
19
8.0
481
Ease of Admin
9.0
8
7.6
159
Quality of Support
9.2
35
8.3
516
Has the product been a good partner in doing business?
9.8
8
8.3
162
Product Direction (% positive)
9.7
35
8.9
542
Features by Category
Reports
Not enough data
8.4
260
Not enough data
8.1
237
Not enough data
8.5
242
Not enough data
8.1
235
Not enough data
8.4
237
Data Preparation
Not enough data
8.4
212
Not enough data
8.4
211
Data Modeling and Blending
Not enough data
8.5
210
Not enough data
8.7
209
Not enough data
8.5
211
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Agentic AI - Analytics Platforms
Not enough data
7.9
93
Not enough data
7.9
91
Not enough data
8.1
92
Not enough data
8.1
90
Not enough data
7.9
90
Not enough data
7.7
90
Not enough data
8.1
90
Self Service
Not enough data
8.3
232
Not enough data
8.4
233
Not enough data
8.5
234
Not enough data
8.1
229
Not enough data
8.1
232
Not enough data
8.2
227
Deployment & Integration - Analytics Platforms
Not enough data
8.4
94
Not enough data
8.3
93
Not enough data
8.1
89
Not enough data
8.4
93
Advanced Analytics
Not enough data
8.5
238
Not enough data
8.8
236
Not enough data
8.6
242
Performance & Scalability - Analytics Platforms
Not enough data
8.5
114
Not enough data
8.4
94
Advanced Analytics & Modeling - Analytics Platforms
Not enough data
8.5
97
Not enough data
8.4
101
Not enough data
8.6
98
Agentic AI Capabilities - Analytics Platforms
Not enough data
7.8
90
Not enough data
7.9
89
Not enough data
8.0
88
Not enough data
7.8
88
Personalized Intelligence - Analytics Platforms
Not enough data
8.1
89
Not enough data
7.9
88
Not enough data
7.8
90
Building Reports
Not enough data
8.5
205
Not enough data
8.5
201
Not enough data
7.8
196
Not enough data
8.0
200
Data Source Access
Not enough data
8.5
118
Not enough data
8.4
120
Not enough data
8.2
115
Data Interaction
Not enough data
8.5
116
Not enough data
8.2
115
Not enough data
8.5
115
Not enough data
8.8
114
Not enough data
8.8
115
Not enough data
8.8
116
Not enough data
8.4
113
Not enough data
8.5
114
Data Exporting
Not enough data
8.4
115
Not enough data
8.3
116
Not enough data
8.5
112
Generative AI
Not enough data
Feature Not Available
Data
Not enough data
8.6
12
Not enough data
8.6
12
Not enough data
8.2
13
Not enough data
8.5
12
Analytics
Not enough data
7.9
12
Administration
Not enough data
8.5
26
Not enough data
8.5
25
Not enough data
8.3
25
Not enough data
8.8
27
Management
Not enough data
8.2
26
Not enough data
8.5
26
Not enough data
8.7
26
Not enough data
9.0
24
Not enough data
7.9
26
Compliance
Not enough data
8.5
25
Not enough data
Feature Not Available
Not enough data
8.4
25
Not enough data
8.6
25
Security
Not enough data
8.7
26
Not enough data
8.3
23
Not enough data
Feature Not Available
Data Quality
Not enough data
8.6
27
Not enough data
8.6
25
Not enough data
8.5
26
Maintainence
Not enough data
8.4
24
Not enough data
Feature Not Available
Generative AI
Not enough data
7.9
25
Not enough data
8.0
25
Agentic AI - Data Governance
Not enough data
9.0
7
Not enough data
9.5
7
Not enough data
9.5
7
Not enough data
9.5
7
Not enough data
9.0
7
Not enough data
9.5
7
Administration
Not enough data
8.9
136
Not enough data
8.9
140
Not enough data
8.8
137
Capabilities
Not enough data
9.3
144
Not enough data
8.9
139
Not enough data
8.7
137
Not enough data
8.6
134
Methodology
Not enough data
8.9
138
Not enough data
9.2
140
Not enough data
9.1
139
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Deployment
9.1
21
8.1
13
8.9
20
8.1
14
8.9
19
8.2
13
8.8
20
8.5
14
9.3
20
8.3
14
Deployment
8.7
19
8.2
14
9.4
18
8.3
13
9.0
20
8.3
13
9.3
18
8.2
14
9.3
17
8.7
13
Management
8.8
18
7.6
13
9.0
18
8.5
13
9.1
18
8.5
13
9.1
17
8.5
14
Operations
9.0
17
8.8
14
9.1
18
7.9
14
9.2
19
8.8
14
Management
8.7
17
7.9
13
8.8
16
8.2
14
8.8
16
8.1
13
Generative AI
8.1
14
Feature Not Available
8.3
15
Feature Not Available
System
Not enough data
8.4
74
Model Development
Not enough data
8.1
89
Not enough data
8.6
82
Not enough data
8.3
82
Not enough data
8.7
82
Model Development
Not enough data
8.3
71
Machine/Deep Learning Services
Not enough data
7.4
76
Not enough data
7.6
78
Not enough data
7.3
76
Not enough data
8.0
78
Machine/Deep Learning Services
Not enough data
7.6
70
Not enough data
7.9
72
Deployment
Not enough data
8.1
80
Not enough data
8.0
77
Not enough data
8.2
82
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
7.7
27
Not enough data
7.3
27
Not enough data
7.5
27
Not enough data
7.2
27
Not enough data
7.1
27
Not enough data
7.1
27
Not enough data
7.5
27
Financial Planning
Not enough data
7.8
10
Not enough data
8.2
10
Not enough data
8.5
10
Agentic AI - FP&A (Financial Planning & Analysis)
Not enough data
Not enough data
Not enough data
Not enough data
Financial Insight
Not enough data
8.0
10
Not enough data
8.8
10
Not enough data
7.9
11
Data
Not enough data
8.6
11
Not enough data
8.5
10
Not enough data
8.2
10
Not enough data
8.5
10
Status Communication
Not enough data
7.8
10
Not enough data
8.3
11
Not enough data
8.2
11
Not enough data
8.5
11
Management
Not enough data
8.8
27
Not enough data
9.0
28
Not enough data
9.3
28
Not enough data
8.9
28
Not enough data
8.8
27
Maintenance
Not enough data
8.7
26
Not enough data
8.9
27
Not enough data
9.0
26
Security
Not enough data
8.9
28
Not enough data
8.6
28
Integration
Not enough data
8.0
20
Not enough data
8.2
19
Business Logic
Not enough data
7.5
19
Not enough data
7.3
19
Not enough data
8.2
19
Management
Not enough data
7.7
18
Not enough data
7.5
19
Analytics
Not enough data
8.4
20
Not enough data
7.6
19
Deployment
Not enough data
8.2
19
Not enough data
7.6
17
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Agentic AI - Decision Management Platforms
Not enough data
7.5
6
Not enough data
6.7
6
Not enough data
6.4
6
Not enough data
6.7
6
Not enough data
6.1
6
Not enough data
6.7
6
Not enough data
7.5
6
Setup
Not enough data
7.7
19
Not enough data
7.5
19
Not enough data
8.0
19
Data
Not enough data
8.3
20
Not enough data
8.6
19
Analysis
Not enough data
8.4
19
Not enough data
7.9
19
Not enough data
7.9
18
Not enough data
8.0
19
Not enough data
7.9
18
Not enough data
7.4
18
Not enough data
7.8
18
Customization
Not enough data
7.6
18
Not enough data
7.5
19
Not enough data
8.1
19
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Integration - Machine Learning
Not enough data
8.3
36
Learning - Machine Learning
Not enough data
8.5
38
Not enough data
8.5
39
Not enough data
8.6
37
Agentic AI - Accounting & Finance
Not enough data
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
8.7
15
Not enough data
8.7
15
Not enough data
9.0
15
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
9.0
15
Not enough data
8.8
15
Not enough data
8.7
15
Statistical Tool
Not enough data
8.6
77
Not enough data
8.7
76
Not enough data
8.6
76
Data Analysis
Not enough data
8.9
76
Not enough data
8.8
76
Decision Making
Not enough data
8.8
80
Not enough data
9.0
80
Not enough data
8.9
79
Not enough data
8.7
77
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Functionality
Not enough data
8.7
67
Not enough data
8.8
66
Not enough data
9.0
64
Not enough data
8.8
64
Not enough data
8.8
63
Management
Not enough data
9.2
66
Not enough data
9.0
62
Not enough data
8.9
64
Not enough data
9.0
62
Not enough data
Feature Not Available
Generative AI
Not enough data
8.3
60
Not enough data
8.3
61
Building Reports
Not enough data
8.5
205
Not enough data
8.5
201
Not enough data
7.8
196
Not enough data
8.0
200
Platform
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
Categories
Categories
Shared Categories
Microsoft Fabric and SAS Viya are categorized as MLOps Platforms
Unique Categories
Microsoft Fabric has no unique categories
SAS Viya is categorized as Statistical Analysis, Data Governance, Event Stream Processing, Data Preparation, Text Analysis, Data Science and Machine Learning Platforms, Machine Learning, Analytics Platforms, Predictive Analytics, Database Management Systems (DBMS), FP&A (Financial Planning & Analysis), Data Quality, Decision Management Platforms, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Microsoft Fabric
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
SAS Viya
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
17.5%
Insurance
15.0%
Manufacturing
7.5%
Education Management
7.5%
Commercial Real Estate
5.0%
Other
47.5%
Pharmaceuticals
12.2%
Banking
9.0%
Higher Education
8.0%
Computer Software
7.9%
Financial Services
6.2%
Other
56.7%
Alternatives
Discussions
Microsoft Fabric has no discussions with answers
What is SAS Visual Data Mining and Machine Learning used for?
1 Comment
DG
SAS Visual Data Mining and Machine Learning is mainly used to analyze large data, find patterns, and build predictive models to support better business...Read more
SAS Viya has no more discussions with answers
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