Label Studio
The most flexible open-source data labeling platform for AI and ML
Founded
2019
Starting Price
\u00240
Category
Developer Tools
Last updated March 19, 2026
About Label Studio
Label Studio is an open-source, multi-modal data labeling platform developed by HumanSignal. It supports annotation of text, images, audio, video, and time series data, making it a versatile tool for training ML models, fine-tuning LLMs, and evaluating AI systems. With configurable labeling interfaces, ML-assisted annotation, and cloud storage integration, it serves both individual practitioners and enterprise teams.
Pros & Cons
Pros
- Supports all major data types (text, image, audio, video, time series) in a single platform
- Highly configurable labeling interface adapts to virtually any annotation task
- Free and open-source Community edition with no artificial feature limits
- Extensive Python SDK and REST API for seamless ML pipeline integration
- Active open-source community with 350,000+ users and regular updates
Key Features
Multi-Modal Data Support
Annotate text, images, audio, video, and time series data from a single platform with task-specific labeling interfaces
Configurable Labeling Interface
Customize labeling layouts with a template-based system and XML-like configuration for any annotation workflow
ML-Assisted Labeling
Integrate custom ML backends to generate pre-annotations and predictions, accelerating the labeling process
Cloud Storage Integration
Connect to AWS S3, Google Cloud Storage, Azure Blob, Databricks, and Redis for seamless data import and export
Python SDK & REST API
Programmatic access for data upload, annotation retrieval, project management, and integration into ML pipelines
Team Collaboration
Multi-user annotation with task assignment, role-based access control, and quality review workflows
Webhook Automation
Trigger external actions on annotation events for automated pipeline orchestration and real-time notifications
Pricing
Community (Open Source)
\u00240
- Multi-modal labeling platform
- Configurable labeling interface
- Self-hosted deployment
- Python SDK & REST API
- ML backend integration
Best For
ML Training Data Preparation
Create labeled datasets across text, image, and audio for training custom machine learning models including NER, object detection, and classification
LLM Fine-Tuning & Evaluation
Annotate instruction-response pairs, evaluate model outputs, and build custom benchmarks for fine-tuning and assessing large language models
Computer Vision Annotation
Label images and video with bounding boxes, polygons, and semantic segmentation for autonomous vehicles, medical imaging, and manufacturing QA
Document Processing & OCR
Annotate PDFs and scanned documents with layout-aware labeling for building intelligent document processing pipelines
