Top 10 Best Labeling Software of 2026

Labelbox is a leading enterprise-grade data labeling platform that enables teams to annotate images, videos, text, sensor data, and more for machine learning training datasets. It offers collaborative workflows, ML-assisted labeling, quality control tools like consensus and benchmarking, and seamless integrations with frameworks like TensorFlow and PyTorch. Designed for scalability, it supports complex ontologies and automation to accelerate data preparation for production AI models.

V7 is an advanced AI-powered data labeling platform that enables teams to annotate images, videos, text, documents, and 3D point clouds with high precision. It streamlines computer vision and NLP workflows through tools like polygons, keypoints, cuboids, and semantic segmentation, enhanced by auto-annotation using foundation models. V7 also provides workflow automation, quality control, collaboration features, and seamless integrations with ML frameworks for scalable production labeling.

Scale AI is a comprehensive data labeling platform designed for creating high-quality training datasets for machine learning models. It provides advanced annotation tools for diverse data types including images, text, video, LiDAR, and audio, supported by a global workforce of expert labelers. The platform emphasizes scalability, quality control through consensus mechanisms, and integration with ML pipelines for efficient data operations.

SuperAnnotate is a powerful data labeling platform optimized for computer vision AI projects, supporting annotations for images, videos, LiDAR, and 3D point clouds with tools like bounding boxes, polygons, keypoints, and semantic segmentation. It streamlines workflows through team collaboration, ML-assisted auto-labeling, and robust quality assurance features to ensure high annotation accuracy. Ideal for scaling data preparation in enterprise ML pipelines, it integrates with popular cloud storage and export formats for seamless model training.

Encord is an end-to-end data development platform specialized for computer vision AI, providing advanced tools for labeling images, videos, and 3D data with support for bounding boxes, polygons, segmentation, and keypoints. It integrates active learning, automated quality checks, data curation, and collaboration features to optimize ML workflows and reduce manual annotation efforts. Designed for enterprise-scale projects, it emphasizes ontology management and reproducibility for high-quality training data.

Label Studio is an open-source data labeling platform designed for annotating machine learning datasets across multiple modalities including images, text, audio, video, and time-series data. It provides customizable labeling interfaces, collaborative workflows, and integrations with ML backends for active learning and pre-annotations. The tool supports exporting annotations in over 40 formats compatible with major ML frameworks like TensorFlow, PyTorch, and Hugging Face.

Prodigy, developed by Explosion AI, is a scriptable annotation tool designed for efficiently labeling data for natural language processing (NLP) tasks such as named entity recognition, text classification, and dependency parsing. It leverages active learning to prioritize uncertain predictions from models like spaCy, reducing annotation effort by focusing on high-value examples. The tool's Python-based recipes allow for highly customizable workflows, making it ideal for integrating into ML pipelines.

CVAT (Computer Vision Annotation Tool) is an open-source platform for labeling images and videos in computer vision projects, supporting a wide range of annotation types like bounding boxes, polygons, keypoints, and tracks. It enables collaborative annotation, AI-assisted labeling via integrated models, and efficient workflow management for teams preparing datasets for machine learning. Deployable on-premises or through cvat.ai's cloud service, it's highly extensible with plugins and APIs.

Supervisely is a comprehensive web-based platform designed for annotating images and videos in computer vision projects, supporting tasks like object detection, semantic segmentation, keypoints, and classification. It offers AI-powered smart tools for automated labeling, collaborative team workflows, and integrations with ML frameworks and cloud storage. Beyond annotation, it provides model training, validation, and deployment features for end-to-end AI development.

Diffgram is an open-source data labeling platform tailored for machine learning teams, supporting annotation for images, videos, text, audio, and more. It offers collaborative workflows, quality assurance tools, ML-assisted labeling, and performance benchmarking to ensure high-quality datasets. Designed for enterprise use, it supports self-hosted deployments for data privacy and scalability in production ML pipelines.