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Compare Azure Machine Learning and TensorFlow 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (50.4% 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)
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningTensorFlow
Star Rating
(134)4.5 out of 5
Market Segments
Small-Business (50.4% of reviews)
Entry-Level Pricing
No pricing available
Learn more about TensorFlowSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that TensorFlow excels in Model Training with a score of 9.3, highlighting its robust capabilities for deep learning applications, while Azure Machine Learning scores 8.7, indicating it may not be as powerful in this area.
- Reviewers mention that Azure Machine Learning offers superior Ease of Use with a score of 8.6 compared to TensorFlow's 7.6, making it more accessible for beginners and those looking for a user-friendly interface.
- G2 users note that TensorFlow shines in Scalability with a score of 9.1, which is crucial for large-scale machine learning projects, whereas Azure Machine Learning scores slightly lower at 8.9, suggesting it may face challenges in handling extensive workloads.
- Users on G2 highlight that Azure Machine Learning provides better Drag and Drop functionality with a score of 8.7, making it easier for users to build models visually, while TensorFlow's score of 7.8 indicates a less intuitive experience in this regard.
- Reviewers mention that TensorFlow's Pre-Built Algorithms score of 8.6 is a strong point, but Azure Machine Learning's score of 8.3 shows it still offers a decent selection, albeit not as extensive as TensorFlow's offerings.
- Users say that Azure Machine Learning's Managed Service score of 8.8 reflects its strong support for cloud-based deployments, while TensorFlow's score of 8.4 suggests it may not be as optimized for managed services, which could impact deployment ease.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableTensorFlow
No pricing availableFree Trial
Azure Machine Learning
No trial information availableTensorFlow
No trial information availableRatings
Meets Requirements
8.5
81
9.2
117
Ease of Use
8.5
80
8.0
120
Ease of Setup
8.3
57
8.3
98
Ease of Admin
8.3
49
7.9
39
Quality of Support
8.6
74
8.7
104
Has the product been a good partner in doing business?
8.6
47
8.3
36
Product Direction (% positive)
9.0
80
9.3
115
Features by Category
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
8.6
70
Model Development
8.6
51
8.9
96
8.9
54
7.2
82
8.3
53
8.8
96
8.7
52
9.2
95
Model Development
8.4
21
9.0
69
Machine/Deep Learning Services
8.1
45
9.1
93
7.9
45
9.0
88
7.8
38
8.8
86
8.2
42
9.4
95
Machine/Deep Learning Services
8.7
21
8.7
64
8.5
21
9.2
67
Deployment
8.8
50
8.5
80
8.7
51
8.7
89
8.9
51
9.0
89
Generative AI
8.5
10
8.0
11
8.2
10
7.7
11
7.5
10
8.0
11
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
7.3
5
Not enough data
7.3
5
Not enough data
8.0
5
Not enough data
8.7
5
Not enough data
8.3
5
Not enough data
7.0
5
Not enough data
7.7
5
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 TensorFlow 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
TensorFlow 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.)
TensorFlow
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
27.9%
Information Technology and Services
20.2%
Research
7.8%
Computer & Network Security
4.7%
Education Management
3.1%
Other
36.4%
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
What is TensorFlow and why it is used?
2 Comments
PS
TensorFlow is an open-source library that lets you generate various models AI/ML/DL models.Read more
TensorFlow has no more discussions with answers
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