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Compare MLKit and Vertex AI 
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At a Glance
Market Segments
Small-Business (50.0% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
MLKit
Star Rating
(13)4.4 out of 5
Market Segments
Small-Business (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about MLKitVertex 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 High Availability with a score of 9.2, which reviewers mention contributes to a more reliable service for critical applications, while MLKit, although strong, scores slightly lower in this area.
- Reviewers mention that MLKit offers superior Ease of Use with a score of 9.3, making it more accessible for small businesses and new users, whereas Vertex AI's score of 8.3 indicates it may require a steeper learning curve.
- G2 users highlight that Vertex AI's AI Inference Speed, rated at 8.6, is a significant advantage for real-time applications, while MLKit's performance in this area, although good, does not match the speed offered by Vertex AI.
- Users on G2 report that MLKit's Quality of Support, rated at 9.2, is a standout feature, with many reviewers praising the responsiveness and helpfulness of the support team, compared to Vertex AI's score of 8.2.
- Reviewers mention that both platforms have strong Model Training capabilities, each scoring 8.5, but users say that Vertex AI's integration with existing Google Cloud services provides a more seamless experience for those already in the Google ecosystem.
- Users say that MLKit shines in its AI Text Summarization feature, scoring 8.7, which many find particularly useful for content-heavy applications, while Vertex AI's similar feature, although effective, does not receive the same level of acclaim.
Pricing
Entry-Level Pricing
MLKit
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
MLKit
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
9.1
9
8.6
359
Ease of Use
9.3
9
8.2
368
Ease of Setup
Not enough data
8.1
291
Ease of Admin
Not enough data
7.9
141
Quality of Support
9.2
8
8.1
335
Has the product been a good partner in doing business?
Not enough data
8.2
135
Product Direction (% positive)
10.0
8
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
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
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
Categories
Categories
Shared Categories
MLKit and Vertex AI are categorized as Machine Learning
Unique Categories
MLKit is categorized as Artificial Neural Network
Reviews
Reviewers' Company Size
MLKit
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
Information Technology and Services
25.0%
Computer Software
16.7%
Consulting
8.3%
Market Research
8.3%
Higher Education
8.3%
Other
33.3%
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
MLKit 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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