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Labelbox AI

AI data labeling platform for training ML models

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  • Free plan available
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What is Labelbox AI?

Labelbox is a platform designed to help teams prepare training data for machine learning models through efficient data labelling. It provides tools to annotate images, text, video, and other data types, which is essential before training AI systems. The platform connects data scientists, labellers, and ML engineers in a single workflow, reducing the time spent on manual annotation tasks. Labelbox offers both automated labelling options and managed services, making it suitable for organisations of varying sizes. Whether you're building a computer vision model, training a language model, or developing other AI applications, the platform aims to simplify the data preparation phase that typically consumes significant project resources.

Key features

Multi-modal data annotation

label images, video, text, audio, and document data within a single interface

Project management tools

organise labelling tasks, set quality standards, and track progress across teams

Automation and model-assisted labelling

reduce manual work by using machine learning to suggest or pre-label data

Quality control and review workflows

implement consensus checks and reviewer approvals before data enters training pipelines

Integration with ML frameworks

export annotated data in formats compatible with popular training platforms

Managed labelling services

option to outsource annotation work to trained labellers through the platform

Pros & cons

Advantages

  • Supports multiple data types in one platform rather than requiring separate tools for different annotation needs
  • Freemium pricing model lets small teams and individual researchers test the platform without upfront costs
  • Includes automation and AI-assisted features to accelerate the labelling process and reduce repetitive work
  • Managed services option removes the burden of hiring and training internal labelling teams

Limitations

  • Free tier likely has limitations on data volume, team size, or advanced features that may restrict use for larger projects
  • Data labelling quality depends heavily on clear instructions and reviewer oversight; the platform is a tool but not a guarantee of good labels
  • Pricing for paid tiers and managed services may become expensive at scale, particularly for large datasets or complex annotation tasks

Use cases

Training computer vision models by annotating images with bounding boxes, polygons, or semantic segmentation masks

Preparing text datasets for NLP models by labelling sentiment, named entities, or categories

Creating training data for autonomous vehicle systems through video annotation and frame-level labelling

Building datasets for object detection or classification in retail, manufacturing, or healthcare applications

Scaling up machine learning projects by outsourcing annotation work whilst maintaining quality standards

Ready to try Labelbox AI?

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Pricing

Free

Free

Core annotation tools, limited project and team size, suitable for small-scale projects and experimentation

Pro

Custom pricing

Expanded data limits, advanced workflows, priority support, and additional integrations

Enterprise

Custom pricing

Unlimited scale, dedicated support, managed labelling services, custom integrations, and service-level agreements

Get started with Labelbox AI

Click through to Labelbox AI and start using it now.

Try Labelbox AI free
  • Free plan available
  • No credit card