Datasaur
NLP data labeling platform with AI-assisted automation, quality workflows, and private LLM options
Label Studio
Flexible, multi-type data labeling platform for modern ML pipelines.
Label Studio lets teams annotate images, audio, text, video, and time-series through an intuitive UI, supporting cloud storage, multi-user projects, and seamless ML model integration.
Overview
Overview
Label Studio is a flexible annotation platform that supports images, audio, text, video, HTML and time-series data. It provides a clean web UI, configurable label formats, and built-in templates, allowing data scientists, ML engineers and annotation teams to create high-quality training sets or refine existing ones. Projects can be organized per team, with multi-user sign-in and role-based access, while imports from local files or cloud storage (AWS S3, Google Cloud) streamline data ingestion. Integration with the Machine Learning SDK lets you connect any model for pre-labeling and prediction comparison, and a REST API enables embedding the service into automated pipelines.
Deployment Options
Label Studio can be run locally via Docker, Docker-Compose (including optional Nginx, PostgreSQL, and MinIO for S3-compatible storage), pip, Poetry or Anaconda, and it also offers one-click cloud deployments on Heroku, Azure or GCP. A free Starter Cloud trial is available for teams that prefer a managed environment. The open-source nature lets you host the platform on-premise for full data control while leveraging community-driven extensions and documentation.
Highlights
Supports images, audio, text, video, and time-series labeling
Multi-user projects with role-based access control
Built-in templates and configurable UI for custom tasks
Integrates with ML models for pre-labeling and prediction comparison
Pros
- Broad data type support for diverse annotation needs
- Flexible deployment: Docker, pip, cloud, or on-premise
- Extensible via Machine Learning SDK and REST API
- Active community with comprehensive documentation
Considerations
- Self-hosting requires managing infrastructure and updates
- Advanced UI customization may need coding knowledge
- Performance depends on chosen database (SQLite vs PostgreSQL)
- No built-in auto-annotation without integrating external models
Managed products teams compare with
When teams consider Label Studio, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams building custom annotation pipelines for varied data
- Organizations needing on-premise control over labeled data
- Projects that want to integrate model predictions into labeling
- Users who prefer a configurable UI over fixed annotation tools
Not ideal when
- One-off labeling tasks where a hosted SaaS solution suffices
- Teams without technical resources to manage Docker or servers
- Scenarios requiring real-time annotation at massive scale out of the box
- Projects that need auto-annotation without additional SDK work
How teams use it
Image classification dataset creation
Annotators label thousands of images via web UI and export COCO format for training a vision model.
Audio transcription for speech recognition
Team tags speech segments, aligns timestamps, and feeds labeled data to improve ASR accuracy.
Pre-labeling with existing model
Connect a model via the ML SDK, auto-populate predictions, and have humans correct them, accelerating dataset refinement.
Integrating labeling into CI pipeline
REST API triggers labeling jobs from data ingestion scripts, enabling automated data quality loops.
Tech snapshot
JavaScript30%
TypeScript29%
Python28%
CSS7%
SCSS4%
HTML2%
Frequently asked questions
How can I install Label Studio?
You can install via Docker, Docker‑Compose, pip, Poetry, Anaconda, or run a one‑click cloud deployment on Heroku, Azure, or GCP.
Which data formats are supported for import?
Label Studio accepts images, audio, video, text, HTML, time-series, and can import from local files or cloud storage (AWS S3, Google Cloud) in JSON, CSV, TSV, RAR, ZIP archives.
Can I use Label Studio with my own machine learning model?
Yes, the Machine Learning SDK lets you connect any model for pre‑labeling, prediction comparison, and active learning workflows.
Is there a hosted cloud version available?
A free Starter Cloud trial is offered, and you can also deploy the open‑source version to cloud providers like Heroku, Azure, or GCP.
How does multi‑user access work?
Users sign up and log in; annotations are tied to their accounts, and you can organize work into multiple projects with role‑based permissions.
Project at a glance
Active- Stars
- 26,201
- Watchers
- 26,201
- Forks
- 3,332
LicenseApache-2.0
Repo age6 years old
Last commityesterday
Self-hostingSupported
Primary languageTypeScript
Last synced yesterday