Introducing G2.ai, the future of software buying.Try now
Compare Azure Machine Learning and SAS Model Manager 
Featured Products
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
Alteryx
Visit Website
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (56.4% 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 Model Manager
Star Rating
(69)4.6 out of 5
Market Segments
Enterprise (56.4% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about SAS Model ManagerSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in Ease of Deployment with a score of 8.3, making it easier for teams to get started quickly compared to SAS Model Manager, which has a lower score of 8.1. Reviewers mention that Azure's streamlined setup process significantly reduces the time to production.
- Reviewers mention that SAS Model Manager shines in Model Training with a score of 8.8, which is higher than Azure's score of 8.7. Users appreciate the robust training capabilities and the variety of pre-built algorithms available in SAS, allowing for more efficient model development.
- G2 users highlight Azure Machine Learning's superior Scalability with a score of 8.3, which is on par with SAS Model Manager's score of 8.1. However, users on G2 note that Azure's infrastructure is better suited for handling large datasets and high-volume workloads, making it a preferred choice for enterprise-level applications.
- Users say that both platforms offer strong Monitoring capabilities, scoring 8.9 each. However, reviewers mention that Azure provides more comprehensive real-time performance metrics, which are crucial for maintaining model accuracy and performance over time.
- Reviewers mention that Azure Machine Learning has a more flexible Language Support with a score of 8.9 compared to SAS Model Manager's score of 8.6. Users appreciate the ability to work with multiple programming languages, which enhances collaboration among data scientists with varying skill sets.
- Users report that SAS Model Manager's Cataloging feature, scoring 7.8, is effective for organizing models, but Azure's approach to Model Registry is more intuitive, allowing for easier version control and management of machine learning models, which is a significant advantage for teams managing multiple projects.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableSAS Model Manager
No pricing availableFree Trial
Azure Machine Learning
No trial information availableSAS Model Manager
Free Trial is available
Ratings
Meets Requirements
8.5
81
8.3
12
Ease of Use
8.5
80
8.0
15
Ease of Setup
8.3
57
6.7
11
Ease of Admin
8.3
49
7.3
8
Quality of Support
8.6
74
8.8
12
Has the product been a good partner in doing business?
8.6
47
7.9
8
Product Direction (% positive)
9.0
80
9.1
13
Features by Category
Deployment
Not enough data
7.7
5
Not enough data
7.8
6
Not enough data
7.8
6
Not enough data
8.3
6
Not enough data
8.3
6
Deployment
Not enough data
7.2
6
Not enough data
7.5
6
Not enough data
8.1
6
Not enough data
8.1
6
Not enough data
8.1
6
Management
Not enough data
7.8
6
Not enough data
8.9
6
Not enough data
8.3
6
Not enough data
8.3
6
Operations
Not enough data
7.5
6
Not enough data
7.8
6
Not enough data
7.8
6
Management
Not enough data
7.8
6
Not enough data
8.9
6
Not enough data
8.3
6
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
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
Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
Not enough data
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 SAS Model Manager 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
SAS Model Manager is categorized as Machine Learning
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
SAS Model Manager
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%
Computer Software
61.8%
Hospital & Health Care
5.5%
Banking
5.5%
Public Safety
3.6%
Mechanical or Industrial Engineering
3.6%
Other
20.0%
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
SAS Model Manager has no discussions with answers
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
