Software Alternatives & Reviews


Pixel perfect image labeling for industrial, medical, and large scale dataset creation. Create ground truth 10 times faster.

V7 Alternatives

The best V7 alternatives based on verified products, community votes, reviews and other factors.
Latest update:

  1. 51

    Build computer vision products for the real world

    Open Source freemium

  2. 28

    Human-powered Data Processing for AI and Automation

  3. NEURONwriter is an AI-powered tool for writing and optimizing content for SEO. With a user-friendly interface and advanced content editor, it is designed to help you quickly write and optimize high-quality SEO-friendly content.

    Try for free Free Trial $19.0 / Monthly ("Bronze", "2 projects”, "25 analyses", "15.000 AI credits" )

  4. 33

    Empowering Enterprises with Custom LLM/GenAI/CV Models.


  5. 21

    Multi-sensor labeling platform for robotics and autonomous driving

    freemium €800.0 / Monthly (Includes 3,600 hours/yr of labeling usage)

  6. 24

    Seamless project management and collaboration for your team.

    Visit website Open Source $12.0 / Monthly (Per user)

  7. 29

    Enterprise grade data platform for AI systems in development and in production.

  8. 22

    Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.

  9. 13

    Powerful and efficient Computer Vision Annotation Tool (CVAT) - opencv/cvat

  10. 19

    BasicAI combines the best of human and machine intelligence to provide high-quality annotated training data that powers the most innovative machine learning.

    Open Source freemium

  11. 13

    Supervisely helps people with and without machine learning expertise to create state-of-the-art...

  12. 15

    Build highly accurate training datasets using machine learning and reduce data labeling costs by up to 70%.

  13. 16

    The World's AI


  14. 19

    Heartex Label Studio powers internal data labeling operations to achieve the most competitive and differentiated ML/AI models at scale.

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