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Compare Encord and V7 Darwin 
At a Glance
Market Segments
Small-Business (51.6% of reviews)
Small-Business (55.8% of reviews)
Entry-Level Pricing
Contact Us
Free
Encord
Star Rating
(63)4.8 out of 5
Market Segments
Small-Business (51.6% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Learn more about EncordV7 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 plansEncord vs V7 Darwin
When assessing the two solutions, reviewers found them equally easy to use. However, V7 Darwin is easier to set up and administer. Reviewers also preferred doing business with V7 Darwin overall.
- Reviewers felt that V7 Darwin meets the needs of their business better than Encord.
- When comparing quality of ongoing product support, reviewers felt that Encord is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of Encord over V7 Darwin.
Pricing
Entry-Level Pricing
Encord
Simple and scalable pricing
Contact Us
Our pricing scales with your organization. Start building out your data annotation & active learning pipelines today.
- Computer vision labeling toolkit
- Model assisted labeling using your own models or our proprietary micro-models
- Automated object tracking and interpolation
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
Encord
Free Trial is available
V7 Darwin
Free Trial is available
Ratings
Meets Requirements
9.3
48
9.5
38
Ease of Use
9.5
49
9.5
38
Ease of Setup
8.5
13
9.5
17
Ease of Admin
7.9
12
9.4
15
Quality of Support
9.9
47
9.6
36
Has the product been a good partner in doing business?
9.4
12
9.9
14
Product Direction (% positive)
9.8
48
9.6
32
Features by Category
Deployment
9.4
6
Not enough data
9.5
7
9.4
6
9.8
7
9.7
5
9.8
8
9.0
7
9.8
8
9.8
7
Deployment
10.0
6
Not enough data
9.5
7
9.3
5
9.5
7
9.7
6
9.6
8
9.2
6
10.0
8
9.8
7
Management
9.8
7
9.3
5
9.8
8
10.0
6
10.0
6
Not enough data
9.8
8
Not enough data
Operations
10.0
6
9.7
6
9.7
6
Not enough data
10.0
9
10.0
6
Management
10.0
7
10.0
5
10.0
8
9.7
5
9.8
7
9.3
5
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
System
9.0
5
Not enough data
Model Development
Not enough data
Not enough data
9.7
6
Not enough data
10.0
5
Not enough data
9.7
6
Not enough data
Model Development
Not enough data
Not enough data
Machine/Deep Learning Services
9.5
7
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
9.7
5
Not enough data
Machine/Deep Learning Services
Not enough data
Not enough data
9.7
5
Not enough data
Deployment
10.0
7
Not enough data
9.8
7
Not enough data
9.5
7
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
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
9.4
33
9.4
21
9.8
32
9.5
24
9.5
33
9.4
21
9.8
31
9.3
22
Automation
9.5
26
9.4
16
9.5
25
9.4
14
Image Annotation
9.6
32
9.3
27
9.3
28
9.4
24
9.2
24
9.1
17
9.7
25
9.2
18
Natural Language Annotation
9.6
15
9.1
13
9.7
15
8.5
9
9.6
17
9.0
10
Speech Annotation
9.6
15
7.7
8
9.5
14
7.5
8
Recognition Type
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Feature Not Available
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Facial Recognition
Not enough data
Not enough data
Not enough data
Not enough data
Labeling
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
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
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
Categories
Categories
Shared Categories
Encord and V7 Darwin are categorized as Data Labeling and MLOps Platforms
Unique Categories
Encord is categorized as Active Learning Tools, Data Science and Machine Learning Platforms, Image Recognition, and Machine Learning
V7 Darwin has no unique categories
Reviews
Reviewers' Company Size
Encord
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
35.5%
Hospital & Health Care
12.9%
Medical Devices
11.3%
Food & Beverages
9.7%
Information Technology and Services
8.1%
Other
22.6%
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
Encord has no discussions with answers
V7 Darwin has no discussions with answers
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