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Compare V7 Darwin and Vertex AI 
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At a Glance
Market Segments
Small-Business (55.8% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
Free
Pay As You Go Per Month
V7 Darwin
Star Rating
(54)4.8 out of 5
Market Segments
Small-Business (55.8% of reviews)
Pros & Cons
Entry-Level Pricing
Free
Browse all 4 pricing plansVertex 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 offers a solid Managed Service rating of 8.2, but reviewers mention that V7 excels with a perfect score of 10.0, highlighting its superior ease of use and management capabilities.
- Reviewers mention that while Vertex AI has a decent Ease of Setup score of 8.2, V7 shines with a score of 9.7, indicating a more user-friendly onboarding process that many users appreciate.
- G2 users report that Vertex AI's Quality of Support is rated at 8.2, but users on G2 consistently praise V7's support with a score of 9.6, emphasizing the responsiveness and helpfulness of their customer service team.
- Users say that Vertex AI's Scalability is rated at 8.9, which is commendable, but reviewers mention that V7 outperforms it with a score of 9.8, making it a better choice for businesses anticipating rapid growth.
- Reviewers mention that Vertex AI's Model Training capabilities are rated at 8.5, while V7 boasts a perfect score of 10.0, indicating that users find V7's pre-built algorithms and training efficiency significantly more effective.
- Users report that Vertex AI's Deployment Flexibility is rated at 8.4, but reviewers highlight V7's superior Framework Flexibility with a score of 9.4, making it easier for developers to integrate into existing workflows.
Pricing
Entry-Level Pricing
V7 Darwin
Free Plan
Free
Try out the core functionalities and set up your workflow for free.
- 1 Workspace
- 10 User Licenses
- 3 Integrated Models
Vertex 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
V7 Darwin
Free Trial is available
Vertex AI
Free Trial is available
Ratings
Meets Requirements
9.5
38
8.6
359
Ease of Use
9.5
38
8.2
368
Ease of Setup
9.5
17
8.1
291
Ease of Admin
9.4
15
7.9
141
Quality of Support
9.6
36
8.1
335
Has the product been a good partner in doing business?
9.9
14
8.2
135
Product Direction (% positive)
9.6
32
9.2
353
Features by Category
Deployment
Not enough data
8.3
73
9.4
6
8.1
74
9.7
5
8.3
74
9.0
7
8.3
70
9.8
7
8.8
70
Deployment
Not enough data
8.4
73
9.3
5
8.3
72
9.7
6
8.4
71
9.2
6
8.5
71
9.8
7
8.7
69
Management
9.3
5
8.3
70
10.0
6
8.5
69
Not enough data
8.0
69
Not enough data
8.1
69
Operations
9.7
6
8.2
69
Not enough data
8.4
70
10.0
6
8.3
70
Management
10.0
5
8.1
68
9.7
5
8.4
69
9.3
5
8.3
68
Generative AI
Feature Not Available
8.2
34
Feature Not Available
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
Quality
9.4
21
Not enough data
9.5
24
Not enough data
9.4
21
Not enough data
9.3
22
Not enough data
Automation
9.4
16
Not enough data
9.4
14
Not enough data
Image Annotation
9.3
27
Not enough data
9.4
24
Not enough data
9.1
17
Not enough data
9.2
18
Not enough data
Natural Language Annotation
9.1
13
Not enough data
8.5
9
Not enough data
9.0
10
Not enough data
Speech Annotation
7.7
8
Not enough data
7.5
8
Not enough data
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
V7 Darwin and Vertex AI are categorized as MLOps Platforms
Unique Categories
V7 Darwin is categorized as Data Labeling
Reviews
Reviewers' Company Size
V7 Darwin
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
19.2%
Research
7.7%
Hospital & Health Care
5.8%
Industrial Automation
3.8%
Other
38.5%
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
V7 Darwin has no discussions with answers
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