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Compare Google Cloud AutoML and H2O 
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At a Glance
Market Segments
Small-Business (45.5% of reviews)
Small-Business (54.5% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Google Cloud AutoML
Star Rating
(22)4.1 out of 5
Market Segments
Small-Business (45.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Google Cloud AutoMLH2O
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 H2OSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that H2O excels in Ease of Use with a score of 9.0, making it more user-friendly for small businesses compared to Google Cloud AutoML, which has a score of 8.6. Reviewers mention that H2O's intuitive interface allows for quicker onboarding and less training time.
- Reviewers mention that H2O's Pre-Built Algorithms score of 9.4 is a standout feature, providing users with a wide range of ready-to-use models. In contrast, Google Cloud AutoML, while also strong, has a slightly lower score of 9.0, indicating that H2O may offer more immediate value for users looking to deploy models quickly.
- G2 users highlight H2O's superior Quality of Support with a score of 8.8, which is significantly higher than Google Cloud AutoML's 7.6. Users report that H2O's support team is responsive and knowledgeable, which is crucial for businesses that may need assistance during implementation.
- Users on G2 note that H2O's Data Ingestion & Wrangling capabilities score 8.5, allowing for efficient data preparation. Google Cloud AutoML, while effective, does not match this level of performance, which can lead to longer setup times for users.
- Reviewers mention that H2O's Model Training score of 9.2 indicates a robust training process, which is essential for achieving high accuracy in predictions. Google Cloud AutoML's score of 9.0 is commendable but suggests that H2O may provide a slight edge in this critical area.
- Users say that H2O's Deployment as a managed service scores 8.1, which is higher than Google Cloud AutoML's 6.9. This difference suggests that H2O may offer a more seamless deployment experience, which is vital for businesses looking to integrate AI solutions quickly.
Pricing
Entry-Level Pricing
Google Cloud AutoML
No pricing availableH2O
No pricing availableFree Trial
Google Cloud AutoML
No trial information availableH2O
Free Trial is available
Ratings
Meets Requirements
8.6
14
8.0
15
Ease of Use
8.6
14
9.0
14
Ease of Setup
7.4
11
7.8
6
Ease of Admin
7.9
12
8.3
6
Quality of Support
7.5
14
8.8
13
Has the product been a good partner in doing business?
8.3
11
8.3
5
Product Direction (% positive)
8.9
11
6.4
13
Features by Category
System
Not enough data
8.5
8
Model Development
Not enough data
8.3
8
Not enough data
8.3
8
Not enough data
9.4
8
Not enough data
9.2
8
Model Development
Not enough data
9.0
8
Machine/Deep Learning Services
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
9.0
5
Machine/Deep Learning Services
Not enough data
Not enough data
Not enough data
9.0
5
Deployment
Not enough data
8.1
6
Not enough data
8.0
5
Not enough data
8.6
6
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
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
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
Google Cloud AutoML and H2O are categorized as Data Science and Machine Learning Platforms
Unique Categories
Google Cloud AutoML is categorized as Low-Code Machine Learning Platforms
H2O is categorized as Artificial Neural Network, Predictive Analytics, and Generative AI Infrastructure
Reviews
Reviewers' Company Size
Google Cloud AutoML
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
Research
13.6%
Information Technology and Services
13.6%
Computer Software
9.1%
Program Development
4.5%
Pharmaceuticals
4.5%
Other
54.5%
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
Google Cloud AutoML has no discussions with answers
H2O has no discussions with answers
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