Compare Azure Machine Learning and H2O 
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (54.5% 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 LearningH2O
Star Rating
(24)4.5 out of 5
Market Segments
Small-Business (54.5% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about H2OAzure Machine Learning vs H2O
When assessing the two solutions, reviewers found H2O easier to use. However, reviewers felt that Azure Machine Learning is easier to set up and do business with overall. Both products were equally easy to administer.
- Reviewers felt that Azure Machine Learning meets the needs of their business better than H2O.
- When comparing quality of ongoing product support, reviewers felt that H2O is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Azure Machine Learning over H2O.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableH2O
No pricing availableFree Trial
Azure Machine Learning
No trial information availableH2O
Free Trial is available
Ratings
Meets Requirements
8.5
81
8.0
15
Ease of Use
8.5
80
9.0
14
Ease of Setup
8.3
57
7.8
6
Ease of Admin
8.3
49
8.3
6
Quality of Support
8.6
74
8.8
13
Has the product been a good partner in doing business?
8.6
47
8.3
5
Product Direction (% positive)
9.0
80
6.4
13
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
8.5
8
Model Development
8.6
51
8.3
8
8.9
54
8.3
8
8.3
53
9.4
8
8.7
52
9.2
8
Model Development
8.4
21
9.0
8
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
9.0
5
Machine/Deep Learning Services
8.7
21
Not enough data
8.5
21
9.0
5
Deployment
8.8
50
8.1
6
8.7
51
8.0
5
8.9
51
8.6
6
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
Core Functionality - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Handling - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Performance - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Usability - Artificial Neural Network
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Features - Artificial Neural Network
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
Agentic AI - Artificial Neural Network
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
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
6
Not enough data
Not enough data
Not enough data
8.3
5
Data Analysis
Not enough data
8.7
5
Not enough data
8.0
5
Decision Making
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
7.0
5
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Azure Machine Learning and H2O are categorized as Data Science and Machine Learning Platforms and Generative AI Infrastructure
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), and Low-Code Machine Learning Platforms
H2O is categorized as Artificial Neural Network and Predictive Analytics
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
H2O
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%
Financial Services
13.6%
Retail
9.1%
Research
9.1%
Marketing and Advertising
9.1%
Information Technology and Services
9.1%
Other
50.0%
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
H2O has no discussions with answers
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