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At a Glance
Market Segments
Enterprise (42.9% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Maple
Star Rating
(15)4.3 out of 5
Market Segments
Enterprise (42.9% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about MapleVertex 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 AIMaple vs Vertex AI
- Reviewers felt that Maple meets the needs of their business better than Vertex AI.
- When comparing quality of ongoing product support, reviewers felt that Maple is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Vertex AI over Maple.
Pricing
Entry-Level Pricing
Maple
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
Maple
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
8.9
11
8.6
359
Ease of Use
8.0
11
8.2
368
Ease of Setup
Not enough data
8.1
291
Ease of Admin
Not enough data
7.9
141
Quality of Support
8.5
8
8.1
335
Has the product been a good partner in doing business?
Not enough data
8.2
135
Product Direction (% positive)
7.9
11
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
Design
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Tools
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Work
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
Environment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
System
Not enough data
8.2
170
Model Development
Not enough data
8.4
202
Not enough data
7.9
179
Not enough data
8.4
200
Not enough data
8.5
202
Model Development
Not enough data
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
Not enough data
8.2
178
Machine/Deep Learning Services
Not enough data
8.5
165
Not enough data
8.4
163
Deployment
Not enough data
8.2
193
Not enough data
8.3
194
Not enough data
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
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
Categories
Categories
Shared Categories
Maple and Vertex AI share no categories
Unique Categories
Maple is categorized as Simulation & CAE
Reviews
Reviewers' Company Size
Maple
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
Higher Education
28.6%
Information Technology and Services
14.3%
Computer Software
14.3%
Research
7.1%
Mechanical or Industrial Engineering
7.1%
Other
28.6%
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
Maple has no discussions with answers
What is Google Cloud AI Platform used for?
2 Comments
KS
Google cloud AI Platform enables us to build Machine learning models, that works on any type and any size of data. Read more
What software libraries does cloud ML engine support?
2 Comments
JP
It's supporting approx all trending libraries.Read more
What is Google AI platform?
1 Comment
ZM
The Google AI Platform is a comprehensive set of tools and services provided by Google Cloud to develop, deploy, and manage artificial intelligence. It...Read more
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