Labelbox
The data factory for AI teams
About Labelbox
Labelbox is an AI data platform that provides end-to-end infrastructure for creating high-quality training and evaluation data for machine learning models. It supports multimodal annotation across text, image, video, and audio, with built-in model-assisted labeling, quality control workflows, and an expert network of 1.5M+ knowledge workers for scalable data operations.
Pros & Cons
Pros
- Supports all major data types — text, image, video, audio, and multimodal — in a single platform
- Generous free tier with up to 30 users and 50 projects for teams getting started
- Model-assisted labeling with foundation models dramatically speeds up annotation workflows
- Purpose-built RLHF and evaluation tools for frontier AI development and LLM fine-tuning
- Comprehensive Python SDK and API for seamless ML pipeline integration
- Access to 1.5M+ expert annotators through Alignerr network for scaling data operations
Cons
- Paid plan pricing is not publicly listed — requires contacting sales for quotes
- Advanced features like SSO, custom embeddings, and auto-labeling locked behind paid tiers
- Platform complexity can be steep for small teams with simple annotation needs
- No self-hosted option — all data goes through Labelbox's cloud infrastructure
- Free tier limited to a single workspace which restricts team organization
Key Features
Multimodal Annotation
10+ built-in editors for labeling text, images, video, audio, and multimodal chat with support for classification, bounding boxes, segmentation, NER, and more
Model-Assisted Labeling
Integrate foundation models and custom models to auto-generate labels, enabling faster annotation with human-in-the-loop review and correction
Data Catalog & Curation
Unified data management with 25+ cloud source integrations, vector and natural language search, and intelligent data exploration and curation tools
Quality Control & Consensus
Built-in quality assurance workflows with real-time feedback, reviewer roles, consensus scoring, and performance monitoring across annotation teams
RLHF & Evaluation Workflows
Purpose-built tools for reinforcement learning from human feedback including preference pairs, reward signals, scoring rubrics, and head-to-head model comparisons
Alignerr Expert Network
Access to 1.5M+ knowledge workers across 40+ countries including 50K+ PhDs for specialized domain-expert annotation and model evaluation tasks
Custom Model Integration
Import custom model predictions for error analysis, active learning, and automated pre-labeling to accelerate annotation pipelines
Python SDK & API
Comprehensive Python SDK and GraphQL API for programmatic access to projects, data management, label import/export, and ML pipeline integration
Pricing
- Up to 30 users
- Up to 50 projects
- Up to 25 ontologies
- 1 workspace
- Core catalog and annotation tools
- Data curation with natural language search
- Model-assisted labeling
- Community support
- Unlimited users
- Unlimited projects and ontologies
- SSO authentication
- Labelbox Monitor
- Custom embeddings
- Foundry models for model-assisted labeling
- Auto-labeling tools
- Live multimodal chat editor for evaluations
- AI critic and labeling quality guarantee
- Premium support
- Everything in Subscription
- Multiple workspaces
- Dedicated account management
- Custom SLAs
- Labelbox Services for managed data labeling
- Alignerr expert network access
- Advanced security and compliance
- Priority support
Best For
Computer Vision Model Training
Create labeled image and video datasets with bounding boxes, segmentation masks, and classifications for training object detection, autonomous driving, and medical imaging models
LLM Fine-Tuning & RLHF
Generate preference pairs, reward signals, and human evaluations for reinforcement learning from human feedback workflows to align and improve large language models
NLP Data Annotation
Label text datasets for named entity recognition, sentiment analysis, text classification, and conversational AI training with collaborative team workflows
Model Evaluation & Benchmarking
Run custom evaluations with domain experts to benchmark model performance, compare models head-to-head, and identify failure modes before deployment
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