Compare Gemini Enterprise Agent Platform and Labelbox 
Featured Products
At a Glance
Market Segments
Small-Business (42.3% of reviews)
Small-Business (45.8% of reviews)
Entry-Level Pricing
Pay As You Go Per Month
No pricing available
Gemini Enterprise Agent Platform
Star Rating
(653)4.3 out of 5
Market Segments
Small-Business (42.3% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Learn more about Gemini Enterprise Agent PlatformLabelbox
Star Rating
(48)4.5 out of 5
Market Segments
Small-Business (45.8% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about LabelboxSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users appreciating its ability to centralize the entire ML lifecycle—from data preparation to deployment. This comprehensive approach significantly reduces the effort needed to build and train models.
- Users say that Labelbox stands out for its user-friendly setup process, with many highlighting how simple it is to get started. The platform's intuitive interface allows for easy access to tools and features right after signing in, making it a smooth experience for project managers and data trainers alike.
- Reviewers mention that while Vertex AI has a robust feature set, it sometimes struggles with specific workflow needs, leading to configuration challenges for some users. This can impact the overall user experience, especially for those with unique requirements.
- According to verified reviews, Labelbox shines in its collaborative features, allowing real-time teamwork on annotation tasks. Users appreciate the customizable labeling tools and support for various data formats, making it particularly suitable for academic AI/ML projects.
- G2 reviewers highlight that Vertex AI's integration with Google Cloud is a significant advantage, simplifying the management of the full ML lifecycle. This seamless integration is a key factor for users who rely on Google services for their data and model management.
- Users express that while both platforms have their strengths, Labelbox's higher ratings in ease of use and support quality indicate a more user-friendly experience overall. This is particularly beneficial for teams looking for a straightforward solution without extensive training or onboarding.
Pricing
Entry-Level Pricing
Gemini Enterprise Agent Platform
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
Labelbox
No pricing availableFree Trial
Gemini Enterprise Agent Platform
No trial information availableLabelbox
Free Trial is available
Ratings
Meets Requirements
8.6
388
9.0
42
Ease of Use
8.1
399
9.0
42
Ease of Setup
8.1
321
8.9
22
Ease of Admin
7.9
150
9.0
5
Quality of Support
8.1
364
9.0
40
Has the product been a good partner in doing business?
8.3
144
8.7
5
Product Direction (% positive)
9.2
382
9.0
36
Features by Category
Deployment
8.4
76
Not enough data
8.1
78
Not enough data
8.3
76
Not enough data
8.4
76
Not enough data
8.8
75
Not enough data
Deployment
8.5
75
Not enough data
8.3
73
Not enough data
8.4
72
Not enough data
8.6
74
Not enough data
8.7
71
Not enough data
Management
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Not enough data
Operations
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Not enough data
Management
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Not enough data
Generative AI
8.4
37
Not enough data
8.6
37
Not enough data
System
8.2
170
Not enough data
Model Development
8.5
206
Not enough data
7.8
179
Not enough data
8.4
204
Not enough data
8.5
206
Not enough data
Model Development
8.2
164
Not enough data
Machine/Deep Learning Services
8.3
201
Not enough data
8.5
200
Not enough data
8.2
197
Not enough data
8.2
178
Not enough data
Machine/Deep Learning Services
8.5
164
Not enough data
8.5
163
Not enough data
Deployment
8.3
210
Not enough data
8.3
200
Not enough data
8.6
205
Not enough data
Generative AI
8.3
106
Not enough data
8.3
103
Not enough data
8.2
103
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
8.0
35
Not enough data
7.8
34
Not enough data
7.6
36
Not enough data
7.8
32
Not enough data
8.4
34
Not enough data
7.4
33
Not enough data
7.6
33
Not enough data
Quality
Not enough data
9.1
27
Not enough data
8.9
27
Not enough data
9.1
28
Not enough data
8.6
26
Automation
Not enough data
8.5
26
Not enough data
8.4
25
Image Annotation
Not enough data
8.7
27
Not enough data
8.6
26
Not enough data
8.7
25
Not enough data
8.8
25
Natural Language Annotation
Not enough data
9.0
24
Not enough data
8.4
23
Not enough data
8.7
21
Speech Annotation
Not enough data
8.4
20
Not enough data
7.9
20
Scalability and Performance - Generative AI Infrastructure
9.0
31
Not enough data
8.7
32
Not enough data
8.6
31
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.0
34
Not enough data
7.7
31
Not enough data
8.1
30
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.5
30
Not enough data
8.3
32
Not enough data
8.5
31
Not enough data
Security and Compliance - Generative AI Infrastructure
8.7
30
Not enough data
8.3
32
Not enough data
8.9
30
Not enough data
Usability and Support - Generative AI Infrastructure
8.2
31
Not enough data
8.5
31
Not enough data
Model Training & Optimization - Active Learning Tools
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 Management & Annotation - Active Learning Tools
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
Model Performance & Analysis - Active Learning Tools
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
8.5
66
Not enough data
Learning - Machine Learning
8.5
64
Not enough data
8.3
63
Not enough data
8.8
64
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
9.0
26
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.8
24
Not enough data
9.0
24
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.8
22
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
9.3
25
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
9.1
24
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
9.2
23
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
9.0
23
Not enough data
8.7
22
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
8.7
23
Not enough data
8.9
22
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.7
21
Not enough data
9.1
22
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.1
22
Not enough data
9.0
23
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
22
Not enough data
Customization - AI Agent Builders
8.6
28
Not enough data
7.6
27
Not enough data
8.3
26
Not enough data
Functionality - AI Agent Builders
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Not enough data
Data and Analytics - AI Agent Builders
7.8
26
Not enough data
7.9
27
Not enough data
8.1
28
Not enough data
Integration - AI Agent Builders
8.8
28
Not enough data
8.2
30
Not enough data
8.1
28
Not enough data
7.5
27
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
Categories
Categories
Shared Categories
Gemini Enterprise Agent Platform and Labelbox are categorized as MLOps Platforms and Data Science and Machine Learning Platforms
Unique Categories
Gemini Enterprise Agent Platform is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Labelbox is categorized as Active Learning Tools and Data Labeling
Reviews
Reviewers' Company Size
Gemini Enterprise Agent Platform
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Labelbox
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
Computer Software
17.6%
Information Technology and Services
14.2%
Financial Services
6.9%
Retail
3.6%
Hospital & Health Care
3.4%
Other
54.3%
Computer Software
31.3%
Information Technology and Services
16.7%
Accounting
4.2%
Automotive
4.2%
Biotechnology
4.2%
Other
39.6%
Alternatives
Discussions
What is Google Cloud AI Platform used for?
3 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?
3 Comments
SA
Google Cloud ML Engine supports many software libraries, including TensorFlow, scikit-learn, XGBoost, Keras, etc....Read more
What is Google AI platform?
2 Comments
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
How do I download Labelbox?
2 Comments
NB
Hi! You can try it for free here https://app.labelbox.com/signup. Read more
Labelbox has no more discussions with answers
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