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Compare Deep Cognition and Vertex AI 
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At a Glance
Market Segments
Small-Business (58.8% of reviews)
Small-Business (41.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Deep Cognition
Star Rating
(17)4.5 out of 5
Market Segments
Small-Business (58.8% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about Deep CognitionVertex 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 Deep Cognition's score of 7.7 in "Managed Service" indicates some users find it less dependable in high-demand scenarios.
- Reviewers mention that Vertex AI's "Model Training" feature is robust, scoring 8.5, which users say allows for efficient training processes, whereas Deep Cognition's "Pre-Built Algorithms" received a lower score of 7.3, suggesting that users find it less versatile in pre-configured solutions.
- G2 users highlight that Vertex AI's "Scalability" is rated at 8.9, making it a preferred choice for businesses anticipating growth, while Deep Cognition's scalability score of 8.1 indicates some limitations in handling larger datasets or user loads.
- Users on G2 report that Vertex AI's "Ease of Setup" is rated at 8.2, which they find straightforward, while Deep Cognition's higher score of 8.5 in this area suggests that users appreciate its user-friendly onboarding process even more.
- Reviewers mention that Vertex AI's "Quality of Support" is rated at 8.2, with many users expressing satisfaction with the assistance provided, while Deep Cognition's score of 8.5 indicates a slightly better support experience, as users report quicker response times and more helpful resources.
- Users say that Vertex AI's "Integration" capabilities score 8.5, which reviewers find beneficial for connecting with other tools, while Deep Cognition's similar score suggests it also offers good integration options, but some users feel it lacks the depth of customization found in Vertex AI.
Pricing
Entry-Level Pricing
Deep Cognition
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
Deep Cognition
No trial information availableVertex AI
Free Trial is available
Ratings
Meets Requirements
8.3
12
8.6
359
Ease of Use
8.3
13
8.2
368
Ease of Setup
8.5
11
8.1
291
Ease of Admin
8.7
10
7.9
141
Quality of Support
8.5
11
8.1
335
Has the product been a good partner in doing business?
8.9
9
8.2
135
Product Direction (% positive)
8.1
12
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
8.3
8
8.4
202
8.3
8
7.9
179
7.3
8
8.4
200
8.5
8
8.5
202
Model Development
Not enough data
8.2
165
Machine/Deep Learning Services
8.6
7
8.2
200
8.8
7
8.4
196
8.9
6
8.2
195
8.3
7
8.2
178
Machine/Deep Learning Services
Not enough data
8.5
165
Not enough data
8.4
163
Deployment
7.7
5
8.2
193
8.6
6
8.3
194
8.1
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
Operating System
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Document Processing
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
Platform Additional Functionality
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - OCR
Not enough data
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
Deep Cognition and Vertex AI are categorized as Data Science and Machine Learning Platforms
Unique Categories
Deep Cognition is categorized as OCR
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Deep Cognition
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
17.6%
Computer Software
17.6%
Higher Education
11.8%
E-Learning
11.8%
Education Management
11.8%
Other
29.4%
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
Deep Cognition has no discussions with answers
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