John Smith 
Data labelling has become a critical process for building robust models in the age of artificial intelligence and machine learning. The quality of labelled data significantly influences model performance, so choosing a data labelling platform is pivotal. This article explores the best data labelling tools available, focusing on features, usability, pricing, and optimal use cases.
Why You Need Data Labeling Software
Data labelling software helps annotate data to train AI models effectively. These tools streamline data annotation workflows, offer collaboration features, and provide advanced labelling capabilities. Whether you’re working with image, video, audio, or text data, choosing the right labelling tool can dramatically improve productivity and accuracy.
Factors to Consider When Choosing a Data Labeling Tool
Selecting the right data labelling software depends on several factors, including:
- Data Types Supported: Some platforms specialize in image labelling, while others excel in text or audio annotation.
- Automation and AI Assistance: Tools with AI-driven annotation can speed up labelling tasks.
- Collaboration Features: Teams working on large projects benefit from real-time collaboration and role-based access.
- Pricing: The cost of the tool should align with your budget, especially if you handle large volumes of data.
- Integration and Export Options: Seamless integration into your existing AI pipeline is essential for maintaining workflow efficiency.
- Ease of Use: An intuitive interface and user-friendly features make the labelling process faster.
- Data Privacy and Security: Compliance with data protection regulations is vital.
How to Choose the Best Data Labeling Software
Here are some practical tips for selecting the ideal data labelling tool for your project:
- Define Your Project Requirements: Start by identifying the data types you need to label (images, text, video, audio).
- Evaluate the Scalability: Check if the software can handle large datasets efficiently.
- Assess the Collaboration Features: If multiple users are involved, prioritize tools that support real-time collaboration and role management.
- Consider Integration Capabilities: Opt for software that integrates smoothly with your AI pipeline and data storage solutions.
- Budget Considerations: Balance your needs with the pricing structure, including hidden costs for additional features or data volumes.
- Customer Support and Community: Look for tools with robust support options and an active user community.
Top Data Labeling Platforms of 2025
SuperAnnotate
SuperAnnotate is a leading data labelling platform designed for complex annotation tasks across multiple data types, including images, videos, and text. Known for its robust AI-powered labelling features, it significantly reduces manual annotation efforts. The platform supports team collaboration, allowing multiple users to work on a single project with role-based access. SuperAnnotate also offers automated pipeline integration, making it an ideal choice for projects that require continuous data input. With a focus on data security and privacy compliance, it’s trusted by industries like healthcare and autonomous vehicles. Its intuitive interface ensures efficient workflow and a minimal learning curve.
Pricing
- Free: It offers basic features such as annotation editors, team and project management, cloud integrations, and up to 3 users.
- Pro: It offers all the free features with additional features such as automation tools, natural language search, one-shot annotation, and pilot services. For pricing, it offers a demo option after requesting.
- Enterprise: It is the custom-tailored plan for the companies.
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Appen
Appen is a globally recognized data labelling tool, particularly known for its large, scalable workforce. It excels in human-in-the-loop labelling, providing accurate annotations for diverse datasets. Appen’s platform is versatile, handling text, speech, image, and video data. It supports multi-language annotation, making it a top choice for global AI projects. The platform also includes quality assurance mechanisms to maintain high labelling accuracy. Appen integrates seamlessly into existing data pipelines, and its scalable solutions accommodate large enterprise projects. Custom annotation templates add flexibility, making it suitable for diverse AI applications, including sentiment analysis and speech recognition.
Pricing: Appen provides the pricing for their products based on the requested service.
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Dataloop
Dataloop is an end-to-end data labelling and management platform designed to streamline the entire AI data pipeline. It supports real-time annotation and pipeline automation, significantly accelerating data processing. Dataloop’s robust collaboration features make it easy for teams to manage complex projects, while data versioning ensures consistent tracking of changes. The platform integrates easily with other AI tools, offering a flexible API for custom workflows. Its project management tools help organize tasks, making it ideal for industries like autonomous vehicles, drone data processing, and retail analytics. Dataloop’s intuitive interface simplifies the entire labelling process.
Pricing: Dataloop provides the pricing for their products based on the requested service.
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Labelbox
Labelbox is a comprehensive data labelling solution known for its intuitive user interface and model-assisted labelling capabilities. It supports multi-format data annotation, including images, videos, and text, making it versatile for various AI projects. Labelbox offers quality control workflows to ensure high labelling accuracy, and its model prediction integration streamlines the annotation process. Collaboration tools allow large teams to work efficiently, while API support enables custom integrations. The platform’s flexibility makes it suitable for complex applications like medical imaging, autonomous vehicles, and retail AI, where accuracy and efficiency are paramount.
Pricing:
- Free: provide 500 LBUs (Labelbox Units) with basic features such as data curation with NLP, multimodal data annotation, a limited number of users, and projects with community support.
- Starter($0.10/LBU): unlimited users and projects, custom labelling, project performance dashboard with advanced support portal access.
- Enterprise: It is the custom-tailored plan for the companies.
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Datature
Datature is a powerful data labelling tool designed specifically for vision AI tasks. It provides a collaborative labelling environment, allowing multiple users to annotate and review data simultaneously. Datature supports both image and video annotation, with built-in model training and validation features. The platform’s visual data management dashboard offers clear insights into project progress. Flexible data import/export options make integration with existing workflows seamless. Datature’s easy-to-use interface reduces the learning curve, making it an excellent choice for projects requiring precise image segmentation, object detection, and video labelling.
Pricing:
- Free: This plan is mainly for developers and researchers to build AI models. It provides features such as 50,000 assets, 3,000 GPU training mins/month, and Python SDK with Slack Community Support.
- Professional: This plan is for teams mainly for scaling the machine learning pipeline and offers features such as more than 50,000 assets, 20,000 GPU Training mins/month, automation workflow, and GPU deployment with dedicated Slack support.
- Enterprise: It is the custom-tailored plan for the companies.
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Encord
Encord is a data labelling software optimized for high-volume projects, offering real-time collaboration and custom workflows. Its automated labelling features reduce manual effort, while quality checks ensure accuracy. The platform includes a project management dashboard for tracking progress and performance metrics. Encord’s integration with existing AI tools makes it highly adaptable for complex projects. It is especially useful for medical data annotation and industrial AI applications, where precision and speed are crucial. Encord’s focus on data tracking and workflow customization makes it ideal for large teams handling extensive datasets.
Pricing:
- Starter: Image & video annotation toolkit, customizable workflows, self-serve support
- Team: Data agents, performance analytics, model evaluation with onboarding support
- Enterprise: It is the custom-tailored plan for the companies.
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Labeller
Labeller is a cost-effective data labelling tool perfect for smaller projects and startups. Its fast labelling process and multiple export formats make it versatile for various data types, including text and images. The platform’s project management tools help organize tasks efficiently, while its easy integration with existing workflows enhances productivity. Labeller’s user-friendly interface minimizes onboarding time, making it accessible even to non-technical users. The cost-efficient subscription model makes it attractive for small businesses needing basic labelling capabilities without sacrificing quality. It’s particularly useful for document classification and simple image tagging tasks.
Pricing:
- Researcher Plan: It is a free plan for the students and researchers. It offers 2,500 files, 1 seat, and 1 workspace with over 100 projects.
- Pro Plan($499/month): A perfect plan for small teams and businesses. It offers features such as 50,000 credits with unlimited projects with human-in-the-loop services.
- Enterprise: It is the custom-tailored plan for the companies.
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Conclusion
Choosing the best data labelling software depends on project complexity, budget, and data types. SuperAnnotate and Appen are optimal for enterprise-level projects, while Labeller is an affordable choice for smaller tasks. Dataloop and Labelbox offer comprehensive solutions for large teams, whereas Datature stands out for vision-specific projects. Encord is highly effective for high-volume video annotation. By evaluating your needs and comparing features, you can select a data labelling tool that enhances your AI development process. The right choice not only ensures efficient labelling but also significantly impacts the performance of your AI models.
Frequently Asked Questions (FAQs)
What is data labelling software?
Data labelling software is a tool that helps annotate data to make it usable for machine learning and AI models. It supports labelling tasks for images, videos, text, and audio data.
Why is data labelling important for AI models?
Accurate data labelling improves model performance by providing high-quality, annotated training data. Poor labelling can lead to inaccurate model predictions.
What factors should I consider when choosing data labelling software?
Key factors include data type support, AI-driven automation, collaboration features, integration capabilities, ease of use, and pricing.
Can data labelling software handle large datasets?
Yes, platforms like Appen and Dataloop are designed to manage large-scale data labelling projects efficiently.
What are the common pricing models for data labelling software?
Pricing models vary, including subscription-based plans, usage-based pricing, and enterprise custom plans. Some tools offer free tiers for basic use.
