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Compare SuperAnnotate and V7 Darwin 
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At a Glance
Market Segments
Small-Business (63.5% of reviews)
Small-Business (55.8% of reviews)
Entry-Level Pricing
Contact Us
Free
SuperAnnotate
Star Rating
(172)4.9 out of 5
Market Segments
Small-Business (63.5% of reviews)
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansV7 Darwin
Star Rating
(54)4.8 out of 5
Market Segments
Small-Business (55.8% of reviews)
Entry-Level Pricing
Free
Browse all 4 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that V7 excels in Labeler Quality with a score of 9.4, while SuperAnnotate shines even brighter with a score of 9.8, indicating that SuperAnnotate provides a more reliable labeling experience.
- Reviewers mention that SuperAnnotate's Quality of Support is rated at 9.9, compared to V7's 9.6, suggesting that users find SuperAnnotate's customer service to be more responsive and helpful.
- G2 users highlight that SuperAnnotate offers superior Ease of Deployment with a perfect score of 10.0, while V7 scores 9.0, indicating that users find SuperAnnotate easier to set up and integrate into their workflows.
- Users on G2 report that SuperAnnotate's Scalability is rated at 10.0, compared to V7's 9.8, suggesting that SuperAnnotate is better suited for growing businesses that need to scale their operations efficiently.
- Reviewers say that SuperAnnotate outperforms V7 in Object Detection, scoring 10.0 versus V7's 9.6, which indicates that users find SuperAnnotate's capabilities in this area to be more advanced and effective.
- Users report that V7 has a strong Human-in-the-Loop feature with a score of 9.4, but SuperAnnotate surpasses it with a score of 9.7, suggesting that users appreciate the enhanced collaborative capabilities in SuperAnnotate.
Pricing
Entry-Level Pricing
SuperAnnotate
Pro
Contact Us
Get ready to scale your most sophisticated AI projects and MLOps needs
- Fully customizable multimodal editor
- Image, video, text, and audio editors
- Data curation and exploration
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
SuperAnnotate
Free Trial is available
V7 Darwin
Free Trial is available
Ratings
Meets Requirements
9.6
113
9.5
38
Ease of Use
9.6
118
9.5
38
Ease of Setup
9.6
78
9.5
17
Ease of Admin
9.6
46
9.4
15
Quality of Support
9.7
115
9.6
36
Has the product been a good partner in doing business?
9.7
47
9.9
14
Product Direction (% positive)
9.5
99
9.6
32
Features by Category
Deployment
9.8
10
Not enough data
9.2
11
9.4
6
9.3
9
9.7
5
9.7
10
9.0
7
9.5
10
9.8
7
Deployment
9.4
11
Not enough data
9.5
11
9.3
5
9.5
10
9.7
6
9.4
11
9.2
6
9.3
12
9.8
7
Management
9.3
9
9.3
5
9.0
10
10.0
6
9.2
8
Not enough data
9.6
8
Not enough data
Operations
9.3
9
9.7
6
9.2
8
Not enough data
9.2
8
10.0
6
Management
9.6
8
10.0
5
9.4
8
9.7
5
9.3
7
9.3
5
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Quality
9.7
76
9.4
21
9.6
74
9.5
24
9.7
74
9.4
21
9.7
68
9.3
22
Automation
9.3
57
9.4
16
9.5
47
9.4
14
Image Annotation
9.3
70
9.3
27
9.4
67
9.4
24
9.3
59
9.1
17
9.5
61
9.2
18
Natural Language Annotation
9.3
46
9.1
13
9.2
39
8.5
9
9.6
43
9.0
10
Speech Annotation
9.2
40
7.7
8
9.0
38
7.5
8
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
9.5
13
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
9.6
13
Not enough data
9.6
13
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
9.4
12
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
9.6
13
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
9.5
13
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
9.4
13
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
9.5
11
Not enough data
9.4
13
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
9.5
13
Not enough data
9.6
12
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
9.4
12
Not enough data
9.5
11
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.5
11
Not enough data
9.4
11
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
9.4
12
Not enough data
Categories
Categories
Shared Categories
SuperAnnotate and V7 Darwin are categorized as Data Labeling and MLOps Platforms
Unique Categories
SuperAnnotate is categorized as Large Language Model Operationalization (LLMOps)
V7 Darwin has no unique categories
Reviews
Reviewers' Company Size
SuperAnnotate
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
Information Technology and Services
22.6%
Computer Software
16.4%
Research
6.9%
Higher Education
5.0%
Health, Wellness and Fitness
3.8%
Other
45.3%
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
What is SuperAnnotate?
1 Comment
MM
SuperAnnotate is an end-to-end platform to annotate, version, and manage ground truth data for your AI.Read more
What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?
1 Comment
ST
I have been invited to skill test for a few projects with superannotate over the past year or so, each resulting in the platform being buggy and not allowing...Read more
SuperAnnotate has no more discussions with answers
V7 Darwin has no discussions with answers
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