5 Best V7 Alternatives in 2024

January 22, 2024
5 mins
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V7 is a known data labeling platform, offering basic functionality with a pretty UI, making it ideal for basic annotation tasks.

Advanced commercial teams will run into various constraints, including:

  • Limitations with data classification
  • Issues with native video rendering
  • Restricted DICOM compatibility
  • Lacks organizational groups and project management
  • No data curation or model evaluation features
  • Pricing structure that may not adapt effectively to scalability

For these reasons, we will explore alternatives to V7 labs. 

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Encord is a leading alternative platform to build annotation, curate visual data, find and fix data errors, and monitor model performance. With its robust features, customizable workflows, and seamless integration with custom models, Encord empowers AI practitioners to build accurate and efficient models.

Encord - V7 Alternative

Encord: Features and Benefits

  • Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform enriched by micro-models, ideal for various annotation and labeling use cases, QA workflows, and training computer vision models.
  • Specifically designed for computer vision applications, Encord offers native support for a wide array of annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more.
  • Encord incorporates active learning pipelines to enhance model performance. By identifying edge cases and gaps in training data, active learning ensures that your models learn from the most informative data.
  • Encord’s DICOM tool is specifically designed for medical imaging annotation. It can handle over 20,000 pixel intensities, far surpassing existing tools. The tool also includes a label review functionality, crucial for supporting FDA approval processes.
  • It also offers specialized features for Synthetic Aperture Radar (SAR) data in geospatial applications.
  • The platform allows you to train micro-models using few-shot learning. These micro-models can auto-annotate large datasets efficiently, saving valuable time during the labeling process.
  • Encord seamlessly integrates MLOps workflows for computer vision and machine learning teams, into your annotation pipeline. Detect anomalies, monitor model performance, and generate augmented data to improve label quality.
  • Encord’s streamlined collaboration features facilitate efficient teamwork. Precise tracking of annotator performance ensures high-quality labels, elevating label quality. Annotator management and quality assurance workflows are integral to maintaining label excellence.

Encord - an alternative to V7 Labs for annotation

  • Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance.
  • An advanced Python SDK and API access, coupled with effortless export capabilities in JSON and COCO formats, enhance flexibility and integration with external systems.
  • Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes.
  • Integrated tagging for data and labels, including outlier tagging.
  • Employs quality metrics (data, label, and model) to assess and improve ML pipeline performance across data curation, data labeling, and model training.

Quality metrics representation


Dataloop is a data labeling service provider renowned for its comprehensive annotation tools and streamlined management solutions.

Dataloop: Features and Benefits

  • Dataloop offers a customizable approach to data annotation, allowing users to tailor their workflows to specific needs. 
  • Gain valuable insights from metrics such as annotator working hours and the number of objects annotated per hour. These analytics help optimize efficiency and quality.
  • Unlike some competitors, Dataloop ensures transparency by clearly presenting its pricing plans on its website. Users can make informed decisions based on their budget and requirements.
  • While Dataloop’s user interface may be less intuitive for beginners, it caters to experienced data professionals who appreciate its robust functionality.


Labeller is another data labelling platform known for its scalability and performance, seamless integration with your existing workflows

Labellerr: Features and Benefits

  • Labeller provides a range of annotation tools to accommodate various data labeling needs, including image annotation, text labeling, and video segmentation. Users can customize annotation workflows to match specific project requirements, ensuring accurate and consistent labeling.
  • Labeler offers robust collaboration features, allowing multiple users to work on the same project simultaneously. 
  • You can create and automate image labeling workflows tailored to your project needs.
  • Labellerr provides comprehensive dashboards to track progress and quality.


Labelbox is a data labeling platform that offers a comprehensive suite of tools and services for annotating and managing datasets. 

Labelbox: Features and Benefits

  • Labelbox provides a variety of annotation tools, including image annotation, text labeling, and video segmentation.
  • Customizable workflows allow users to tailor annotations to specific project requirements.
  • Multiple users can work on the same project simultaneously, enhancing productivity and teamwork.
  • Users can create and automate image labeling workflows based on project needs.


iMerit is a data labeling service provider known for its annotations and management solutions. Unlike traditional labeling platforms, iMerit offers a service-based approach to data annotation.

iMerit: Features and Benefits

  • Customizable solution for annotation, analysis, categorization, segmentation needs.
  • Get insights from metrics such as the annotator's working hours, the number of objects per hour and more.
  • iMerit also provides a free trial for it’s users, but has no mention of it’s pricing plan on it’s website.
  • iMerit’s user interface may be less intuitive and user-friendly for beginners. 

TELUS International

TELUS International, formerly Playment, is a Labelbox alternative that focuses on specialized data labeling services, offering features tailored to specific use cases, ensuring user comfort.

TELUS International: Features and Benefits

  • TELUS International allows the creation of custom data labeling workflows, ensuring that even the most specialized projects can be accommodated.
  • The platform has review and feedback loops to maintain the accuracy of annotations.
  • CX support in 50+ languages across all traditional and digital channels.
  • Integration with other tools and platforms, allows workflow management and collaboration. 
  • These features allow to accommodate the growing needs of businesses, ensuring that the platform can handle increasing data volumes and complexity. 
  • There are limited integration options with other third-party software and systems, which may hinder the ability to streamline processes across different platforms. 
  • Potential challenges in adapting to the training data platform's interface and functionalities, requiring additional training datasets and support for users to fully utilize its capabilities. 


CVAT, or Computer Vision Annotation Tool, is an open-source platform tailored for data annotation, particularly in the field of computer vision. It stands out as a community-driven solution for data labeling.

CVAT: Features and Benefits

  • It's a fantastic choice for startups, research projects, and academic initiatives, thanks to its open-source nature. 
  • CVAT is a cost-effective and highly adaptable alternative to Labelbox
  • Being open-source, CVAT encourages community contributions and customization. It's a collaborative tool, making it accessible for a wide range of users, from newbies to pro.
  • The process of dataset curation, annotation, training, and dataset improvement is the heart of data-centric AI.
  • CVAT has capabilities for bounding boxes, polygons, and keypoint labeling.
  • Users can adapt CVAT to their specific needs, through custom plugins, tailored workflows, or support for new data types.
  • While CVAT offers a wide range of annotation tools, it does not have all the advanced features that some users may require for their specific annotation tasks. 

Pareto AI

Pareto AI distinguishes itself from typical data labeling platforms by prioritizing AI researchers and skilled workers, focusing on complex, customized tasks rather than mass-scale, simple labeling. This approach ensures high-quality data and satisfies both parties, avoiding the pitfalls of low-quality data and unfair worker incentives.

Pareto AI: Features and Benefits

  • Pareto recruits and onboards highly skilled, motivated talent and builds sourcing funnels for niche skill sets if your project demands it.
  • Pareto enhances operations through a working model that boosts productivity and data quality by offering fair incentives, encouraging worker feedback, and enabling direct requester communication, leading to refined tasks and more efficient outcomes.
  • It swiftly launches projects requiring expert judgment by efficiently assembling vetted experts and initiating work within weeks.
  • The company can help with LLM prompting, Reinforcement Learning from Human Feedback (RLHF), data classification & indexing, engine annotation, creative hallucination, honesty & factuality training, LLM fluency, and relevancy grading.

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Written by

Nikolaj Buhl

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Frequently asked questions
  • Encord Annotate, Labelbox, Dataloop, Labellerrare great alternatives to V7.These alternatives cater to diverse needs, making them strong contenders for those seeking efficient data annotation solutions.

  • V7 serves as a tool for data annotation and labeling. It enables us to create precise annotations for images, videos, and other data types, which are essential for training machine learning models.

  • The customizability of alternative V7 Labs tools varies based on the specific platform. Some alternatives, like Encord Annotate, offer seamless integration with existing data pipelines, allowing users to tailor workflows to their needs. Others, such as Dataloop, provide APIs for more flexible integration. It ultimately depends on the specific requirements and preferences of the user’s data annotation workflow.

  • V7 is primarily an annotation and labeling platform. It prepares high-quality labeled data for training ML models. While it doesn’t directly train models, its output (annotated data) serves as input for training platforms like TensorFlow, PyTorch, or custom ML pipelines.