Compare Azure Machine Learning and SAS Enterprise Miner 
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (57.6% 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 LearningSAS Enterprise Miner
Star Rating
(192)4.2 out of 5
Market Segments
Enterprise (57.6% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about SAS Enterprise MinerAzure Machine Learning vs SAS Enterprise Miner
When assessing the two solutions, reviewers found Azure Machine Learning easier to use, set up, and administer. Reviewers also preferred doing business with Azure Machine Learning overall.
- Reviewers felt that SAS Enterprise Miner meets the needs of their business better than Azure Machine Learning.
- When comparing quality of ongoing product support, reviewers felt that Azure Machine Learning is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Azure Machine Learning over SAS Enterprise Miner.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableSAS Enterprise Miner
No pricing availableFree Trial
Azure Machine Learning
No trial information availableSAS Enterprise Miner
No trial information availableRatings
Meets Requirements
8.5
81
8.7
168
Ease of Use
8.5
80
7.7
168
Ease of Setup
8.3
57
7.7
48
Ease of Admin
8.3
49
7.7
47
Quality of Support
8.6
74
8.2
151
Has the product been a good partner in doing business?
8.6
47
8.3
47
Product Direction (% positive)
9.0
80
7.4
159
Features by Category
Administration
Not enough data
7.8
6
Not enough data
7.2
6
Not enough data
6.4
6
Capabilities
Not enough data
7.2
6
Not enough data
8.1
6
Not enough data
7.8
6
Not enough data
7.5
6
Methodology
Not enough data
8.9
6
Not enough data
8.9
6
Not enough data
8.3
6
Generative AI
Not enough data
6.7
6
Not enough data
6.7
6
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
8.6
22
8.1
8
Model Development
8.6
51
7.1
8
8.9
54
7.7
8
8.3
53
8.8
8
8.7
52
8.5
8
Model Development
8.4
21
8.1
8
Machine/Deep Learning Services
8.1
45
8.3
7
7.9
45
8.1
7
7.8
38
8.3
7
8.2
42
8.3
8
Machine/Deep Learning Services
8.7
21
8.1
7
8.5
21
7.6
7
Deployment
8.8
50
8.6
7
8.7
51
8.8
7
8.9
51
9.0
7
Generative AI
8.5
10
8.6
7
8.2
10
8.6
7
7.5
10
8.8
7
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
Statistical Tool
Not enough data
8.1
86
Not enough data
8.3
93
Not enough data
8.0
89
Data Analysis
Not enough data
9.1
103
Not enough data
8.4
103
Decision Making
Not enough data
8.6
97
Not enough data
7.2
103
Not enough data
8.0
100
Not enough data
7.8
17
Generative AI
Not enough data
7.4
7
Not enough data
7.4
7
Categories
Categories
Shared Categories
Azure Machine Learning and SAS Enterprise Miner are categorized as Data Science and Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, and Low-Code Machine Learning Platforms
SAS Enterprise Miner is categorized as Predictive Analytics and Statistical Analysis
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
SAS Enterprise Miner
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%
Higher Education
17.4%
Information Technology and Services
11.4%
Hospital & Health Care
10.9%
Research
7.6%
Government Administration
6.0%
Other
46.7%
Alternatives
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
What is advanced analytics platform?
1 Comment
GS
Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of...Read more
SAS Enterprise Miner has no more discussions with answers
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