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10 Best Data Annotation and Data Labeling Tools in 2024

Choosing the right data annotation tools can be challenging. In this post, we evaluated 10 leading data labeling platforms on the market.




Admon Foster

You'll surely agree that choosing the right data annotation tools can be very tricky:

Data types, annotation features, quality controls, cost...

In the evolving AI era, high-quality training data has become the key determinant of model performance.

However, faced with a plethora of data labeling tools and services on the market, many teams struggle to quickly identify the solution that best fits their needs.

10 Best Data Annotation and Data Labeling Tools in 2024

So we evaluated 10 leading data labeling tools on the market (presented in no particular order). By examining their strengths, limitations, and optimal application scenarios, we aim to help teams find the most suitable product for their specific needs.

It's crucial to recognize that AI annotation projects have diverse requirements, and the criteria for determining the "best" platform may vary (see "How to Find the Right Data Labeling Tools").

Top 10 Data Labeling Tools in 2024: Overview

We'll examine the leading platforms from 10 data annotation companies in more detail, providing an overview of their key features, highlights, and disadvantages for machine learning teams.

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BasicAI Cloud

BasicAI Cloud is an all-in-one smart data annotation platform that simplifies the labeling process using intelligent tools and algorithms. Users can annotate various data modalities and leverage AI-assisted labeling to boost annotation efficiency significantly. The platform's 3D Sensor Fusion annotation suite is industry-leading, making it ideal for creating AI training datasets in computer vision fields such as autonomous driving and robotics.

Data Labeling Tool – BasicAI Cloud

🧩 Data types: Supports image and video, audio, 3D sensor fusion, 4D-BEV, text, and PDF data types.

💡 Highlights: Best-in-class features for large LiDAR Fusion dataset annotation. Smart annotation tools for auto-labeling, segmentation, object tracking, and speech recognition. Scalable task & project management with sophisticated team collaboration features and automated quality control.

😔 Disadvantages: Despite its extensive features, BasicAI Cloud lacks open API support and integrations with popular platforms like Databricks, Webhook, and TensorFlow, which may hinder its compatibility with existing workflows and tools.

💰 Pricing: Free Plan (all features, 10,000 one-time model calls, 5 seats), Team Plan ($9/seat/month), Enterprise Plan, and On-prem Deployment by contacting sales.

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Ango Hub

Developed by iMerit, Ango Hub is a versatile data annotation platform for enterprise AI. Built on the quality-first principle, it enables AI/ML teams to get ground truth faster, more accurately, and efficiently.

Data Labeling Tool – Ango Hub

🧩 Data types: Supports image, audio, text, video, and PDF data types.

💡 Highlights: Supports a variety of data types and offers flexibility through its plugin system by which users can extend the platform's functionality to suit their specific needs, allows selecting specific annotation examples for reference, and ensures quality by annotator consensus.

😔 Disadvantages: Lacks several key annotation tools and built-in auto-annotation and tracking features, which may require users to rely on plugins or external tools to fill those gaps.

💰 Pricing: Free Trial without a time limit, Custom Plans, and On-prem Options by contacting sales.

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The SuperAnnotate platform offers sophisticated annotation and quality assurance, data management, AI-assisted labeling, native integrations, and data governance, enabling businesses to build datasets and ML pipelines. It caters to teams of all sizes, from small groups to large enterprises, for effectively building, fine-tuning, iterating, and managing AI models.

Data Labeling Tool – SuperAnnotate

🧩 Data types: Supports image, video data, LiDAR data, and text.

💡 Highlights: Provides a solid range of data types and annotation tools, along with auto-annotation and tracking functionalities, making it a balanced choice for projects needing a mix of manual and automated annotation.

😔 Disadvantages: Lacks the support for certain data types, audio transcription, and advanced 3D capabilities, which may limit its applicability for projects with specific requirements in those areas.

💰 Pricing: Free Plan with 5,000 items and 3 users, Custom Pro Plan, and Enterprise Plan by contacting sales.

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V7's data annotation tool, Darwin, boasts a user-friendly interface and supports image labeling, video annotation, documents, and medical imaging files. It offers powerful annotation and model training tools that automate the image labeling process and update model performance in real-time. The platform also provides extensive collaboration and project management features for efficient teamwork.

Data Labeling Tool – V7

🧩 Data types: Supports image, video, and PDF data types.

💡 Highlights: Comprehensive medical imaging annotation suite, efficient AI-powered image labeling and segmentation, and highly customizable collaborative workflows.

😔 Disadvantages: Supports fewer data modalities and is more niche in application, making it less suitable for projects requiring comprehensive annotation across various data types.

💰 Pricing: Free Plan with 1,000 files and 3 seats, Starter Plan (>=$900/month), and multiple Business Plans by contacting sales.

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The Kili platform supports the annotation of text, image, and video data for labeling datasets used in LLMs, generative AI, and CV models. It offers an intuitive user interface and powerful API, allowing users to rapidly annotate training data, find and fix dataset issues, and streamline labeling operations. The platform also enables users to customize data annotation workflows to suit different project requirements.

Data Labeling Tool – Kili

🧩 Data types: Supports image, video, text, and PDF data types.

💡 Highlights: Enables model pre-labeling using ChatGPT and SAM, allows users to create specific annotation workflows tailored to their project needs, and offers webhooks to trigger actions like initiating model training or version control.

😔 Disadvantages: Offers limited annotation toolset and lack of support for audio and advanced 3D functionalities may restrict its flexibility for projects with more complex annotation requirements.

💰 Pricing: Free Plan with 100 annotations and 2 seats, Grow Plan, and Enterprise Plans by contacting sales.

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The Supervisely platform originated from the company's internal tools and is designed to accelerate machine learning R&D. It boasts a rich set of annotation tools, seamless team collaboration features, and an integrated data management system. The platform supports automated annotation and model training, expediting AI project development.

Data Labeling Tool – Supervisely

🧩 Data types: Supports image, video, DICOM, and LiDAR data types.

💡 Highlights: The platform integrates a wide range of state-of-the-art neural network models, allowing users to train, serve, and apply models directly within the platform, as well as visualize, analyze, and improve model performance.

😔 Disadvantages: Does not support non-visual data types such as text and audio, which can be a significant limitation in diverse data projects, and its learning curve for new users (esp. non-tech people) may be steeper compared to simpler annotation tools.

💰 Pricing: 30-Day Free Plan with 2 seats, Pro Plan (>=€199/month), and Enterprise Plan by contacting sales.

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Dataloop's AI development platform is known for its flexibility and scalability. It provides streamlined data management and processing tools to help enterprises maintain efficiency and control throughout the data processing lifecycle, addressing the complexities and challenges of AI and data management to drive AI development.

Data Labeling Tool – Dataloop.AI

🧩 Data types: Supports image, video, audio, text, and LiDAR data types.

💡 Highlights: Provides an intuitive drag-and-drop interface for building data pipelines, and offers training task capabilities, making it suitable for projects that involve both annotation and model training.

😔 Disadvantages: Lacks support for certain data types (such as PDF, HTML) and annotation tools, as well as its absence of built-in auto-annotation or tracking features, may limit its applicability for some projects.

💰 Pricing: Contact sales for a custom quote.

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Labelbox is a data-centric AI platform for building intelligent applications. It supports data annotation, management, and model training supervision for image, video, text, and geospatial data. The platform allows team collaboration and integrates machine learning models to enhance annotation efficiency.

Data Labeling Tool – Labelbox

🧩 Data types: Supports image, video, audio, text, and PDF data types.

💡 Highlights: Offers a user-friendly interface, supports a wide range of data types (especially the geospatial image data), and provides robust collaboration features that allow users to create custom workflows based on specific attributes or requirements.

😔 Disadvantages: Lacks some smart annotation tools and 3D capabilities, which may limit its usefulness for projects requiring complex annotations or 3D data handling.

💰 Pricing: Free Plan with 500LBUs/month, Starter Plan ($0.1/LBU), and Enterprise Plan by contacting sales.

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The SegmentsAI platform specializes in multi-sensor data tagging, offering precise annotation tools and AI-assisted features to accelerate the labeling process. The platform's design emphasizes user experience and provides collaboration and project management tools.

Data Labeling Tool – SegmentsAI

🧩 Data types: Supports image, video, text, and LiDAR data types.

💡 Highlights: Enables workflows using selection and multi-step assignment techniques, and specializes in handling 3D datasets with advanced tools for segmentation and merging of point clouds.

😔 Disadvantages: Lacks support for several data types and annotation tools, as well as advanced collaboration features, which may limit its usefulness for projects with diverse data types or complex teamwork requirements.

💰 Pricing: Team Plan ($9,600/year with 3,600 hours of labeling usage), Scale Plan and Enterprise Plan by contacting sales.

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The Encord platform stands out for its support of large datasets and high-security requirements. It offers a customizable workflow, robust API, and integration options to cater to specific industry needs. The platform is suitable for securely developing, testing, and deploying predictive and generative AI systems at scale.

Data Labeling Tool – Encord

🧩 Data types: Supports images and video data types.

💡 Highlights: Offers a streamlined platform focusing on essential data types and annotation tools, with the integrated active learning pipelines, model evaluation, and model fine-tuning capabilities.

😔 Disadvantages: Lacks support for advanced data types, annotation tools, and 3D features, which may not meet the needs of projects requiring more sophisticated annotation capabilities.

💰 Pricing: Contact sales for Starter Plan, Team Plan, and Enterprise Plan.


Features Comparison Among the 10 Platforms

In the following sections, we will analyze the characteristics and strengths of these platforms across five dimensions, helping you choose the most suitable annotation tool for your team. As mentioned earlier, the "best" platform depends on your project's specific requirements.

Supported Data Types

Data Labeling Tools Comparison – Supported Data Types

All platforms support image and video annotation, as these data types have a wide range of applications and high demand. For projects involving autonomous driving and robotics, LiDAR point cloud annotation is crucial. BasicAI Cloud stands out with industry-leading capabilities in 3D sensor fusion data annotation, supporting large-scale point cloud continuous frame data and 4D-BEV data labeling. This showcases its technical prowess in this domain and makes it an excellent choice for companies working on cutting-edge autonomous driving and robotics projects.

As NLP applications gain traction, the demand for speech and text annotation continues to rise. Labelbox, BasicAI Cloud, and Ango Hub provide comprehensive support for these data types. For medical AI projects, V7 and Labelbox support the annotation of specialized medical imaging data.

Supported Annotation Tools and Tasks

While all platforms support basic annotation tools like bounding boxes, polygons, and semantic segmentation, there are differences in their focus and advanced capabilities.

Data Labeling Tools Comparison – Supported Annotation Tools and Annotation Tasks

For 3D perception algorithms, such as those for autonomous driving or drone obstacle avoidance, cuboid annotation tools are essential. 7 platforms support this, with BasicAI Cloud also enabling simultaneous instance annotation and semantic segmentation of point cloud data.

Keypoint and skeleton annotation tools, vital for human pose estimation and gesture recognition, are supported by most platforms. However, skeleton annotation is less common, available in V7, BasicAI Cloud, and SuperAnnotate.

In practical applications, simultaneously annotating object categories, attributes, and relationships is often necessary to construct an ontology and support higher-level semantic understanding. Platforms like BasicAI Cloud and Labelbox support classification, attribute, and relation labeling tools, enabling comprehensive ontology construction for tasks requiring higher-level semantic understanding.

Intelligence and Model-Friendliness

Data Labeling Tools Comparison – Model Capabilities

Annotation platforms are integrating AI technologies to enhance efficiency. Most platforms support 2D automatic annotation and segmentation, which greatly reduce manual workload, particularly suitable for scenarios with fixed object categories and large quantities such as face and vehicle detection.

However, 3D data automatic annotation and segmentation are rare, with BasicAI Cloud being the only platform among the ten compared to offer this capability. It incorporates fine-tuned state-of-the-art models and has strong technical expertise in 3D perception, making it a viable option for teams engaged in related research.

For continuous frame object tracking, four platforms support 2D automatic object tracking, while BasicAI Cloud also supports 3D sensor fusion object tracking. Joint annotation and fusion of 2D and 3D data are becoming a trend, with BasicAI Cloud, Supervisely, and Dataloop supporting 2D-3D object tracking.

Top platforms like V7, Labelbox, and BasicAI Cloud have robust model integration functions, providing user-friendly interfaces for model management, customization, and other supporting capabilities.

Team Collaboration System

Efficient collaboration is crucial for annotation teams. Most platforms support custom workflows, with V7, Labelbox, and BasicAI Cloud covering a variety of common scenarios and allowing flexible adjustments. It is worth mentioning that BasicAI Cloud offers highly detailed performance reports, making it ideal for annotation businesses handling projects.

Data Labeling Tools Comparison – Team Collaboration System

Quality assurance (QA) is critical, and all platforms provide manual review functionality. BasicAI Cloud, Encord, and Ango Hub go further by offering automated QA tools that allow users to predefine QA rules for real-time or batch checking of annotation results, automatically identifying unqualified annotations.

Clear role division and flexible permission management are essential for collaboration. V7, BasicAI Cloud, and Dataloop have rich role systems. BasicAI Cloud uniquely supports user-defined roles and permissions, and it also allows external teams to be integrated at any stage of the annotation process with unified management.

Top platforms like BasicAI Cloud, V7, and Labelbox excel in both functional completeness and user experience design. BasicAI Cloud stands out with its custom workflows, automatic QA, and batch management features.

Other Features

Data Labeling Tools Comparison – Other Features

Platforms provide comprehensive data processing experiences through auxiliary functions. Most support annotated data import and cloud storage integration, with V7 and BasicAI Cloud offering export version management.

Some platforms prioritize high-value data annotation (V7, Labelbox) or provide natural language search (Labelbox). BasicAI Cloud's automated processing pipeline simplifies end-to-end annotation workflows.

Innovative features like ChatGPT-based annotation assistance (Kili) and integrated training tasks (Encord, Dataloop) showcase the potential for AI technology to empower data annotation.

These auxiliary functions reflect the platforms' commitment to intelligence, automation, and seamless user experiences, opening up new possibilities for AI-driven data annotation.


In this post, we conducted an in-depth comparison of the top 10 data annotation tools worth paying attention to in 2024. We hope you can more clearly understand the strengths and characteristics of different platforms. While there is no one-size-fits-all solution, as the best platform depends on each team's specific data types and task requirements, BasicAI Cloud stands out as a noteworthy and intelligent annotation platform. With its comprehensive data format and task type coverage, AI-driven features, and strong focus on team collaboration, BasicAI Cloud offers an efficient and cost-effective solution for teams of all sizes seeking to improve their annotation workflow. Now, the platform offers a very affordable option for small teams and individual developers: a permanent free account with 10,000 automatic annotation quotas can be obtained upon registration.



This article presents a comparative analysis of selected data annotation platforms based on publicly available information at the time of writing. The inclusion of platforms and the "Top 10" designation are based on the author's research and judgment. While every effort has been made to ensure the accuracy and reliability of the information provided, it is essential to acknowledge that the features, capabilities, pricing, and other details discussed in the article may have evolved since publication. This article does not purport to be an exhaustive resource covering all existing annotation tools or a detailed examination of every aspect of the included platforms. Information provided is for general informational purposes and may not reflect the most recent updates. Readers are encouraged to conduct their own research and evaluate their specific requirements before selecting an annotation solution. Opinions expressed are those of the author and do not necessarily represent the official stance of any mentioned companies.

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