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At a Glance
Market Segments
Small-Business (54.5% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
H2O
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 H2OVertex AI
Star Rating
(592)4.3 out of 5
Market Segments
Small-Business (41.0% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Free Trial is available
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Vertex AI excels in "AI Model Training Scalability," achieving a score of 8.5, which allows for efficient handling of large datasets and complex models. In contrast, H2O, while strong, scores slightly lower at 9.2 for "Model Training," indicating a more robust training capability that some users find beneficial for their needs.
- Reviewers mention that H2O's "Pre-Built Algorithms" feature stands out with a score of 9.4, making it easier for users to implement machine learning solutions without extensive coding. Vertex AI, with a score of 8.4 in the same category, offers fewer pre-built options, which may require more customization and development time.
- G2 users highlight the "Ease of Use" in H2O, scoring 9.0, which is particularly appreciated by small businesses looking for straightforward implementation. Vertex AI, while still user-friendly at 8.3, may present a steeper learning curve for those unfamiliar with AI tools.
- Users on G2 report that Vertex AI's "AI Inference Speed" is rated at 8.6, which is crucial for real-time applications. H2O, however, does not provide a specific score for inference speed, leading some users to feel uncertain about its performance in time-sensitive scenarios.
- Reviewers mention that H2O's "Data Handling" capabilities, particularly in "Data Preprocessing," score 8.5, which is essential for preparing data for analysis. Vertex AI, with a score of 8.3 in "Data Ingestion & Wrangling," may not be as efficient in this area, potentially impacting the overall workflow for users.
- Users say that Vertex AI's "Product Direction" is rated highly at 9.1, indicating a strong commitment to future enhancements and user feedback. In contrast, H2O's score of 6.4 suggests that some users feel less confident about the platform's long-term development and support.
Pricing
Entry-Level Pricing
H2O
No pricing availableVertex AI
Try Vertex AI Free
Pay As You Go
Per Month
New customers get $300 in free credits to spend on Vertex AI.
- A unified UI for the entire ML workflow
- Pre-trained APIs for vision, video, natural language, and more
- End-to-end integration for data and AI
Free Trial
H2O
Free Trial is available
Vertex AI
Free Trial is available
Ratings
Meets Requirements
8.0
15
8.6
359
Ease of Use
9.0
14
8.2
368
Ease of Setup
7.8
6
8.1
291
Ease of Admin
8.3
6
7.9
141
Quality of Support
8.8
13
8.1
335
Has the product been a good partner in doing business?
8.3
5
8.2
135
Product Direction (% positive)
6.4
13
9.2
353
Features by Category
Deployment
Not enough data
8.3
73
Not enough data
8.1
74
Not enough data
8.3
74
Not enough data
8.3
70
Not enough data
8.8
70
Deployment
Not enough data
8.4
73
Not enough data
8.3
72
Not enough data
8.4
71
Not enough data
8.5
71
Not enough data
8.7
69
Management
Not enough data
8.3
70
Not enough data
8.5
69
Not enough data
8.0
69
Not enough data
8.1
69
Operations
Not enough data
8.2
69
Not enough data
8.4
70
Not enough data
8.3
70
Management
Not enough data
8.1
68
Not enough data
8.4
69
Not enough data
8.3
68
Generative AI
Not enough data
8.2
34
Not enough data
8.4
34
System
8.5
8
8.2
170
Model Development
8.3
8
8.4
202
8.3
8
7.9
179
9.4
8
8.4
200
9.2
8
8.5
202
Model Development
9.0
8
8.2
165
Machine/Deep Learning Services
Not enough data
8.2
200
Not enough data
8.4
196
Not enough data
8.2
195
9.0
5
8.2
178
Machine/Deep Learning Services
Not enough data
8.5
165
9.0
5
8.4
163
Deployment
8.1
6
8.2
193
8.0
5
8.3
194
8.6
6
8.5
193
Generative AI
Not enough data
8.3
102
Not enough data
8.2
102
Not enough data
8.1
103
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
34
Not enough data
7.8
34
Not enough data
7.7
34
Not enough data
7.8
34
Not enough data
8.4
34
Not enough data
7.8
34
Not enough data
7.9
34
Scalability and Performance - Generative AI Infrastructure
Not enough data
8.9
28
Not enough data
8.6
28
Not enough data
8.5
28
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.2
28
Not enough data
7.8
28
Not enough data
7.9
28
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.4
28
Not enough data
8.1
28
Not enough data
8.3
28
Security and Compliance - Generative AI Infrastructure
Not enough data
8.6
28
Not enough data
8.5
28
Not enough data
8.9
28
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
28
Not enough data
8.3
28
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
Integration - Machine Learning
Not enough data
8.5
67
Learning - Machine Learning
Not enough data
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
8.9
23
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Not enough data
8.9
22
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
22
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
22
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
22
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
8.7
21
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
21
Not enough data
8.8
21
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
21
Not enough data
9.0
21
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
8.9
22
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
22
Customization - AI Agent Builders
Not enough data
8.5
27
Not enough data
7.6
27
Not enough data
8.3
26
Functionality - AI Agent Builders
Not enough data
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Data and Analytics - AI Agent Builders
Not enough data
7.7
25
Not enough data
7.9
27
Not enough data
8.0
27
Integration - AI Agent Builders
Not enough data
8.7
27
Not enough data
8.0
27
Not enough data
8.0
27
Not enough data
7.5
27
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
8.1
6
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
Data Analysis
8.7
5
Not enough data
8.0
5
Not enough data
Decision Making
9.0
5
Not enough data
Not enough data
Not enough data
8.3
5
Not enough data
7.0
5
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
H2O and Vertex AI are categorized as Data Science and Machine Learning Platforms and Generative AI Infrastructure
Unique Categories
H2O is categorized as Artificial Neural Network and Predictive Analytics
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
H2O
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Vertex AI
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
Financial Services
13.6%
Retail
9.1%
Research
9.1%
Marketing and Advertising
9.1%
Information Technology and Services
9.1%
Other
50.0%
Computer Software
17.5%
Information Technology and Services
13.9%
Financial Services
7.0%
Retail
3.8%
Hospital & Health Care
3.4%
Other
54.4%
Alternatives
Discussions
H2O has no discussions with answers
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