Quick Overview
- 1#1: Labelbox - Collaborative platform for labeling images, text, video, and sensor data with automation and quality controls.
- 2#2: Scale AI - Enterprise-grade data labeling platform delivering high-quality training data at massive scale.
- 3#3: V7 - AI-assisted annotation tool for computer vision tasks on images, videos, and documents.
- 4#4: Prodigy - Active learning annotation tool for NLP, images, and custom tasks with scripting support.
- 5#5: Supervisely - Computer vision platform with advanced labeling, neural networks, and workflow automation.
- 6#6: Label Studio - Open-source multi-type data labeling tool supporting text, images, audio, and video.
- 7#7: Encord - Active learning platform for annotating and curating multimodal data for AI models.
- 8#8: SuperAnnotate - End-to-end annotation platform for computer vision with automation and team collaboration.
- 9#9: CVAT - Open-source web-based tool for annotating video and images in computer vision projects.
- 10#10: Dataloop - MLOps platform with integrated data labeling, pipelines, and model deployment features.
Tools were evaluated based on multi-modal capabilities, automation efficiency, quality control features, user-friendliness, and alignment with varied use cases—balancing enterprise scalability, open-source flexibility, and AI assistance to deliver comprehensive value.
Comparison Table
Explore a comparison table of leading data labeling software, including Labelbox, Scale AI, V7, Prodigy, Supervisely, and more, to understand their key features, workflows, and use cases, helping readers find the right tool for their projects.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | Labelbox Collaborative platform for labeling images, text, video, and sensor data with automation and quality controls. | enterprise | 9.6/10 | 9.8/10 | 9.1/10 | 9.3/10 |
| 2 | Scale AI Enterprise-grade data labeling platform delivering high-quality training data at massive scale. | enterprise | 9.2/10 | 9.5/10 | 8.6/10 | 8.4/10 |
| 3 | V7 AI-assisted annotation tool for computer vision tasks on images, videos, and documents. | specialized | 8.7/10 | 9.2/10 | 8.1/10 | 8.3/10 |
| 4 | Prodigy Active learning annotation tool for NLP, images, and custom tasks with scripting support. | specialized | 8.7/10 | 9.4/10 | 7.2/10 | 8.5/10 |
| 5 | Supervisely Computer vision platform with advanced labeling, neural networks, and workflow automation. | specialized | 8.6/10 | 9.2/10 | 7.8/10 | 8.3/10 |
| 6 | Label Studio Open-source multi-type data labeling tool supporting text, images, audio, and video. | other | 8.7/10 | 9.2/10 | 8.0/10 | 9.5/10 |
| 7 | Encord Active learning platform for annotating and curating multimodal data for AI models. | enterprise | 8.4/10 | 9.1/10 | 7.9/10 | 7.7/10 |
| 8 | SuperAnnotate End-to-end annotation platform for computer vision with automation and team collaboration. | specialized | 8.7/10 | 9.2/10 | 8.0/10 | 8.0/10 |
| 9 | CVAT Open-source web-based tool for annotating video and images in computer vision projects. | other | 8.4/10 | 9.2/10 | 7.1/10 | 9.6/10 |
| 10 | Dataloop MLOps platform with integrated data labeling, pipelines, and model deployment features. | enterprise | 8.4/10 | 9.2/10 | 7.8/10 | 8.0/10 |
Collaborative platform for labeling images, text, video, and sensor data with automation and quality controls.
Enterprise-grade data labeling platform delivering high-quality training data at massive scale.
AI-assisted annotation tool for computer vision tasks on images, videos, and documents.
Active learning annotation tool for NLP, images, and custom tasks with scripting support.
Computer vision platform with advanced labeling, neural networks, and workflow automation.
Open-source multi-type data labeling tool supporting text, images, audio, and video.
Active learning platform for annotating and curating multimodal data for AI models.
End-to-end annotation platform for computer vision with automation and team collaboration.
Open-source web-based tool for annotating video and images in computer vision projects.
MLOps platform with integrated data labeling, pipelines, and model deployment features.
Labelbox
enterpriseCollaborative platform for labeling images, text, video, and sensor data with automation and quality controls.
Model-assisted labeling and active learning integration that closes the ML feedback loop directly within the platform
Labelbox is a leading end-to-end data labeling platform that enables teams to annotate, manage, and curate high-quality training data for AI and machine learning models across images, video, text, sensor, and document data types. It offers customizable labeling interfaces, automation via pre-labeling models, quality control tools like consensus and adjudication, and seamless integrations with ML frameworks such as TensorFlow and PyTorch. Designed for scalability, Labelbox supports collaborative workflows, analytics dashboards, and active learning to streamline the data labeling process from prototyping to production.
Pros
- Extensive support for multimodal data types with advanced annotation tools
- Powerful automation features like model-assisted labeling and active learning
- Enterprise-grade quality assurance, analytics, and workflow orchestration
Cons
- Pricing can escalate quickly for high-volume projects
- Initial setup and ontology configuration have a learning curve
- Free tier limitations may not suffice for complex enterprise needs
Best For
Enterprise AI/ML teams needing scalable, high-precision data labeling with robust collaboration and automation for production-grade models.
Pricing
Free Community plan; Pro starts at $5/user/month + pay-per-annotation (e.g., $0.01-$0.10 per task); Enterprise custom pricing with volume discounts.
Scale AI
enterpriseEnterprise-grade data labeling platform delivering high-quality training data at massive scale.
Data Engine: a unified platform for labeling, curation, evaluation, and active learning loops with built-in ML model assistance
Scale AI is a leading data labeling platform that provides high-quality annotation services for machine learning datasets across images, text, video, audio, and 3D data. It combines AI-powered pre-labeling tools with a global workforce of expert annotators to handle complex tasks in computer vision, NLP, and generative AI. The platform emphasizes scalability, quality control via consensus and audits, and seamless integrations with ML workflows for enterprises building production-grade models.
Pros
- Superior data quality with expert human annotators and AI-assisted tools
- Scalable for massive datasets with advanced ontology management and automation
- Comprehensive quality assurance including audits, consensus, and benchmarking
Cons
- Premium pricing that may be prohibitive for startups or small teams
- Steeper learning curve for custom workflows and advanced configurations
- Custom quotes required, lacking transparent self-serve options
Best For
Enterprise AI teams requiring high-precision, large-scale data labeling for complex ML models.
Pricing
Custom enterprise pricing based on data volume, task complexity, and required quality; typically starts in the thousands per month—contact sales for quotes.
V7
specializedAI-assisted annotation tool for computer vision tasks on images, videos, and documents.
AI Auto-Annotate with trainable models and zero-shot capabilities like Segment Anything for rapid, accurate labeling.
V7 is an advanced data labeling platform tailored for machine learning workflows, specializing in annotation for images, videos, text, audio, and documents. It offers AI-powered auto-labeling, customizable pipelines, quality assurance tools, and team collaboration features to accelerate data preparation. With seamless integrations to cloud storage and ML frameworks, V7 enables scalable labeling operations for complex AI projects.
Pros
- Powerful AI auto-labeling with models like SAM and custom training
- Highly customizable workflows and QA analytics
- Strong collaboration and scalability for teams
Cons
- Higher pricing for small teams or individuals
- Steeper learning curve for advanced customizations
- Primarily excels in computer vision over other modalities
Best For
Mid-to-large ML engineering teams handling computer vision datasets that require AI-assisted, scalable labeling with robust workflows.
Pricing
Free tier for individuals; Pro at $65/user/month (billed annually); Enterprise custom with volume discounts.
Prodigy
specializedActive learning annotation tool for NLP, images, and custom tasks with scripting support.
Active learning that prioritizes high-value examples to minimize labeling volume
Prodigy (prodi.gy) is a scriptable annotation tool from Explosion AI, optimized for NLP and computer vision tasks using active learning to suggest the most informative examples for labeling. It enables rapid dataset creation through customizable Python recipes, supporting tasks like NER, text classification, image segmentation, and dependency parsing. Seamless integration with spaCy allows for end-to-end ML workflows, from annotation to training.
Pros
- Active learning dramatically reduces labeling effort
- Highly customizable via Python scripts
- Excellent performance and speed for iterative workflows
Cons
- Steep learning curve requires Python proficiency
- Command-line focused with limited GUI polish
- Per-user licensing can add up for teams
Best For
NLP and CV teams with programming expertise seeking efficient, model-integrated annotation.
Pricing
$390 per user/year for standard license; enterprise options with volume discounts available.
Supervisely
specializedComputer vision platform with advanced labeling, neural networks, and workflow automation.
Integrated Neural Networks Marketplace for training and deploying custom models directly for auto-labeling
Supervisely is a powerful data labeling platform tailored for computer vision workflows, supporting annotation of images, videos, 3D point clouds, and volumetric data. It provides AI-assisted tools like smart segmentation, object tracking, and auto-labeling to accelerate the annotation process. The platform emphasizes team collaboration with version control, task management, and integrations into ML pipelines, making it suitable for enterprise-scale projects.
Pros
- Advanced AI-assisted labeling with pre-trained models and custom NN training
- Excellent support for multi-modal data including 3D and videos
- Robust collaboration tools and on-premise deployment options
Cons
- Steeper learning curve for complex features and 3D annotation
- Higher pricing for enterprise-scale usage
- Primarily focused on computer vision, less versatile for other data types
Best For
Computer vision teams and enterprises needing scalable, collaborative annotation with AI assistance for images, videos, and 3D data.
Pricing
Free Community edition; Pro starts at €19/user/month (billed annually); Enterprise custom pricing with on-prem options.
Label Studio
otherOpen-source multi-type data labeling tool supporting text, images, audio, and video.
Declarative labeling interface builder allowing custom annotation UIs for any task type without deep coding expertise
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 a flexible, web-based interface for creating custom labeling configurations via a declarative XML-like syntax, enabling complex annotation tasks like object detection, NER, and audio transcription. The tool supports team collaboration, quality control workflows, and integration with ML models for active learning and pre-annotation to accelerate labeling efficiency.
Pros
- Versatile support for diverse data types and annotation tasks
- Highly customizable interfaces and extensibility via plugins
- Strong integration with ML backends for assisted labeling
Cons
- Self-hosting requires DevOps knowledge and infrastructure management
- Steep learning curve for advanced custom configurations
- Enterprise features and cloud scalability come at additional cost
Best For
Development teams and researchers needing a flexible, open-source platform for complex, multi-modal data labeling in ML projects.
Pricing
Free open-source Community Edition; Label Studio Cloud offers a free tier (up to 5k objects), paid plans starting at $39/user/month, with custom Enterprise pricing.
Encord
enterpriseActive learning platform for annotating and curating multimodal data for AI models.
Active Learning engine that prioritizes the most valuable data for labeling to optimize model training efficiency
Encord is an enterprise-grade data labeling platform specialized for computer vision, enabling efficient annotation of images, videos, and 3D data for AI/ML workflows. It provides automation tools like auto-labeling, active learning, and quality control to accelerate dataset curation while ensuring high accuracy. The platform supports collaborative labeling, custom ontologies, and integrations with popular ML frameworks, making it ideal for scaling data operations in CV projects.
Pros
- Advanced automation including active learning and auto-labeling to minimize manual effort
- Superior support for complex data types like videos and 3D point clouds
- Strong collaboration tools and quality assurance workflows for team-based projects
Cons
- Enterprise pricing can be steep for small teams or startups
- Steeper learning curve for advanced features and custom ontologies
- Primarily focused on computer vision, with less emphasis on other modalities like text or audio
Best For
Mid-to-large teams building computer vision AI models that need scalable, automated labeling with robust quality controls.
Pricing
Custom enterprise pricing based on data volume, users, and features; free developer plan available for small projects, with paid tiers starting around $500/month.
SuperAnnotate
specializedEnd-to-end annotation platform for computer vision with automation and team collaboration.
Model-assisted labeling with integrated active learning for continuous improvement
SuperAnnotate is an enterprise-grade data labeling platform specialized in computer vision, enabling precise annotation of images, videos, 3D point clouds, and LiDAR data. It offers advanced tools like polygons, semantic segmentation, keypoints, and cuboids, enhanced by AI-assisted labeling to boost productivity. The platform includes robust project management, quality assurance workflows, and analytics for team collaboration and dataset optimization.
Pros
- AI-assisted annotation and active learning accelerate labeling by up to 10x
- Comprehensive QA tools and analytics ensure high data quality
- Scalable for teams with multi-format support including 3D and video
Cons
- Steep learning curve for advanced features
- Enterprise pricing can be prohibitive for startups or small teams
- Primarily focused on computer vision, with less emphasis on NLP or tabular data
Best For
Enterprise teams and ML engineers requiring precise, scalable annotation for complex computer vision projects.
Pricing
Custom quote-based pricing; starts around $1,000/month for pro plans, with free trial and community edition available.
CVAT
otherOpen-source web-based tool for annotating video and images in computer vision projects.
Sophisticated video annotation with track interpolation and temporal consistency for handling long sequences efficiently
CVAT (cvat.ai) is an open-source, web-based annotation platform specialized for computer vision data labeling, supporting images, videos, and 3D point clouds. It provides extensive tools like bounding boxes, polygons, keypoints, cuboids, and track annotations with interpolation for efficient video labeling. Designed for collaborative workflows, it includes quality control, automatic annotation via AI assistants, and integration with ML pipelines.
Pros
- Highly capable annotation tools for images, videos, and 3D data with AI-assisted labeling
- Fully open-source and free for self-hosting, highly customizable
- Strong support for team collaboration, task management, and quality assurance
Cons
- Steep learning curve for beginners due to dense feature set
- Self-hosting requires technical expertise; cloud version has limitations on free tier
- User interface feels somewhat outdated and less polished than commercial competitors
Best For
Computer vision teams and researchers needing a powerful, cost-free solution for large-scale video and image annotation projects.
Pricing
Free open-source self-hosted version; CVAT Cloud offers a free community tier, Pro at $49/user/month, and enterprise custom pricing.
Dataloop
enterpriseMLOps platform with integrated data labeling, pipelines, and model deployment features.
Automation Engine that orchestrates hybrid human-ML labeling pipelines with active learning
Dataloop is an enterprise-grade MLOps platform with advanced data labeling tools designed for annotating large datasets across computer vision, NLP, audio, and tabular data. It streamlines labeling through ML-assisted automation, collaborative workflows, and quality control mechanisms like consensus and analytics. Beyond basic annotation, it integrates seamlessly into full ML pipelines for data management, versioning, and model training.
Pros
- Powerful ML-assisted labeling and automation to reduce manual effort
- Scalable collaboration and quality assurance for enterprise teams
- Deep integration with MLOps pipelines for end-to-end workflows
Cons
- Steep learning curve for non-expert users
- Enterprise pricing may not suit small teams or startups
- Customization requires developer expertise
Best For
Enterprise ML teams managing high-volume, complex data labeling within full MLOps pipelines.
Pricing
Free Community edition available; paid Enterprise plans are custom-quoted starting from ~$10K/year with free trial.
Conclusion
This review underscores the top tools that drive effective data labeling, with Labelbox leading as the best choice, offering strong collaboration, automation, and quality features. Scale AI stands out for enterprise-scale needs, delivering high-quality data efficiently, while V7 excels with AI-assisted annotation for computer vision. Together, they demonstrate the range of solutions available to build robust AI training data.
Start your AI journey with Labelbox—its intuitive platform and powerful tools make it the go-to for accurate, scalable data labeling that fuels impactful models.
Tools Reviewed
All tools were independently evaluated for this comparison