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Compare Clarifai and V7 Darwin 
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At a Glance
Market Segments
Small-Business (61.5% of reviews)
Small-Business (55.8% of reviews)
Entry-Level Pricing
Free 1000 operations
Free
Clarifai
Star Rating
(65)4.3 out of 5
Market Segments
Small-Business (61.5% of reviews)
Pros & Cons
Entry-Level Pricing
Free 1000 operations
Browse all 4 pricing plansV7 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 plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Clarifai excels in its Object Detection capabilities, scoring 9.0, while V7 shines even brighter with a score of 9.6, indicating a more robust feature set for identifying and classifying objects in images.
- Reviewers mention that V7's Human-in-the-Loop feature, rated at 9.4, significantly enhances the quality of machine learning models by allowing human oversight, whereas Clarifai's score of 7.9 suggests it may not offer the same level of user involvement.
- G2 users highlight that V7's Facial Analysis functionality is top-notch, achieving a perfect score of 10.0, while Clarifai's score of 8.3 indicates it may not provide the same depth of analysis for facial recognition tasks.
- Users on G2 report that V7's Model Training efficiency is superior, with a perfect score of 10.0, compared to Clarifai's score of 8.4, suggesting that V7 offers a more streamlined and effective training process for machine learning models.
- Reviewers mention that V7's Monitoring capabilities are exceptional, scoring 10.0, which allows for real-time performance tracking, while Clarifai's score of 9.3 indicates it may not provide the same level of oversight for model performance.
- Users say that V7's Data Quality features, rated at 9.3, are more comprehensive than Clarifai's 8.8, suggesting that V7 offers better tools for ensuring the integrity and accuracy of data used in machine learning processes.
Pricing
Entry-Level Pricing
Clarifai
Clarifai Community
Free
1000 operations
We offer a free version of our platform for our community to discover, develop and share models, workflows, and datasets.
- Access to 100s of models from Clarifai, Facebook, Google, Hugging Face, Microsoft, Princeton NLP, Salesforce
- Easy UI with a “drag-n-drop” workflow graph editor
- Access to community shared workflows, datasets, and apps
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
Free Trial
Clarifai
Free Trial is available
V7 Darwin
Free Trial is available
Ratings
Meets Requirements
8.2
58
9.5
38
Ease of Use
8.2
58
9.5
38
Ease of Setup
7.7
35
9.5
17
Ease of Admin
8.8
20
9.4
15
Quality of Support
8.1
49
9.6
36
Has the product been a good partner in doing business?
8.5
18
9.9
14
Product Direction (% positive)
7.8
53
9.6
32
Features by Category
Deployment
Not enough data
Not enough data
Not enough data
9.4
6
Not enough data
9.7
5
Not enough data
9.0
7
Not enough data
9.8
7
Deployment
Not enough data
Not enough data
Not enough data
9.3
5
Not enough data
9.7
6
Not enough data
9.2
6
Not enough data
9.8
7
Management
Not enough data
9.3
5
Not enough data
10.0
6
Not enough data
Not enough data
Not enough data
Not enough data
Operations
Not enough data
9.7
6
Not enough data
Not enough data
Not enough data
10.0
6
Management
Not enough data
10.0
5
Not enough data
9.7
5
Not enough data
9.3
5
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
System
8.4
17
Not enough data
Model Development
8.7
18
Not enough data
7.8
18
Not enough data
9.2
18
Not enough data
8.4
18
Not enough data
Model Development
8.8
15
Not enough data
Machine/Deep Learning Services
8.7
18
Not enough data
9.1
16
Not enough data
8.4
16
Not enough data
8.4
17
Not enough data
Machine/Deep Learning Services
8.8
16
Not enough data
8.5
16
Not enough data
Deployment
8.2
17
Not enough data
8.0
17
Not enough data
8.0
15
Not enough data
Generative AI
8.8
15
Not enough data
8.7
15
Not enough data
8.3
15
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
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
Quality
8.3
13
9.4
21
8.7
13
9.5
24
8.9
12
9.4
21
8.1
12
9.3
22
Automation
7.9
12
9.4
16
7.8
12
9.4
14
Image Annotation
7.9
11
9.3
27
8.3
12
9.4
24
8.3
11
9.1
17
8.3
11
9.2
18
Natural Language Annotation
7.9
11
9.1
13
8.1
9
8.5
9
7.8
9
9.0
10
Speech Annotation
7.3
8
7.7
8
7.7
8
7.5
8
Recognition Type
8.3
12
Not enough data
9.1
15
Not enough data
8.7
14
Not enough data
7.6
12
Not enough data
8.1
12
Not enough data
7.8
13
Not enough data
8.2
11
Not enough data
7.9
13
Not enough data
Facial Recognition
8.5
13
Not enough data
7.8
12
Not enough data
Labeling
8.3
12
Not enough data
8.6
12
Not enough data
9.1
13
Not enough data
Deployment
8.6
13
Not enough data
Scalability and Performance - Generative AI Infrastructure
8.4
22
Not enough data
8.1
21
Not enough data
9.0
21
Not enough data
Cost and Efficiency - Generative AI Infrastructure
7.9
22
Not enough data
8.4
20
Not enough data
Feature Not Available
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.1
19
Not enough data
7.9
20
Not enough data
8.5
21
Not enough data
Security and Compliance - Generative AI Infrastructure
8.8
19
Not enough data
8.5
19
Not enough data
Feature Not Available
Not enough data
Usability and Support - Generative AI Infrastructure
7.9
20
Not enough data
Feature Not Available
Not enough data
Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Usability - Emotion AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Performance Optimization - Emotion AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security & Compliance - Emotion AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Features - Emotion AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Detection Features - Emotion AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Emotion AI
Not enough data
Not enough data
Not enough data
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
Clarifai and V7 Darwin are categorized as Data Labeling
Reviews
Reviewers' Company Size
Clarifai
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
V7 Darwin
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
23.1%
Information Technology and Services
12.3%
Higher Education
9.2%
Education Management
4.6%
Online Media
3.1%
Other
47.7%
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%
Alternatives
Discussions
Clarifai has no discussions with answers
V7 Darwin has no discussions with answers
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