Check out Check out release 1.7 with Improved Audio Labeling

Quick Start

Database setup

Label Studio uses a database to store project data and configuration information.

Labeling performance

The SQLite database might work well for projects with tens of thousands of labeling tasks, as long as you don’t plan on using complex filters in the data manager and other complex multi-user pipelines. If you want to annotate millions of tasks or anticipate a lot of concurrent users or your plan to work on real life projects, use a PostgreSQL database. See Install and upgrade Label Studio for more.

For example, if you import data while labeling is being performed, labeling tasks can take more than 10 seconds to load and annotations can take more than 10 seconds to perform. If you want to label more than 100,000 tasks with 5 or more concurrent users, consider using PostgreSQL or another database with Label Studio.

SQLite database

Label Studio uses SQLite by default. You don’t need to configure anything. Label Studio stores all data in a single file in the specified directory of the admin user. After you start Label Studio, the directory used is printed in the terminal.

PostgreSQL database

You can also store your tasks and completions in a PostgreSQL database instead of the default SQLite database. This is recommended if you intend to frequently import new labeling tasks, or plan to label hundreds of thousands of tasks or more across projects.

Create connection on startup

Run the following command to launch Label Studio, configure the connection to your PostgreSQL database, scan for existing tasks, and load them into the app for labeling for a specific project.

label-studio start my_project --init -db postgresql

You must set the following environment variables to connect Label Studio to PostgreSQL:


Create connection with Docker Compose

When you start Label Studio using Docker Compose, you start it using a PostgreSQL database:

docker-compose up -d

Data persistence

If you’re using a Docker container, Heroku, or another cloud provider, you might want your data to persist after shutting down Label Studio. You can export your data to persist your labeling task data and annotations, but to preserve the state of Label Studio and assets such as files that you uploaded for labeling, set up data persistence.

Persist data with Docker

Mount Docker volumes on your machine to persist the internal SQLite database and assets that you upload to Label Studio after you terminate a Docker container running Label Studio.

If you’re starting a Docker container from the command line, use volumes to persist the data. See the Docker documentation for Use volumes. For example, replace the existing volume flag in the Docker command with a volume that you specify:

docker run -it -p 8080:8080 -v <yourvolume>:/label-studio/data heartexlabs/label-studio:latest

If you’re using Docker Compose with the config included in the Label Studio repository, you can set up Docker volumes in the docker-compose.yml file for Label Studio:

version: "3.3"
    image: heartexlabs/label-studio:latest
    container_name: label_studio
      - 8080:8080
      - ./mydata:/label-studio/data


For more about specifying volumes in Docker Compose, see the volumes section of the Docker Compose file documentation.

Persist data with a cloud provider

Host a PostgreSQL server that you manage and set up the PostgreSQL environment variables with Label Studio to persist data from a cloud provider such as Heroku, Amazon Web Services, Google Cloud Services, or Microsoft Azure.