Data Analysis

Label Studio

Label Studio provides configurable data annotation and model-evaluation workflows, with free self-hosted Community and separate hosted or enterprise plans.

Updated: October 24, 2026.

Existing image: an earlier Label Studio homepage showing version 1.11.0 and a March 12, 2024 commit banner. The capture date is unrecorded; this is historical product identification, not the current release or a test performed for this article.

Name	Label Studio
Overview	Label Studio is an open-source data labeling platform that facilitates the enhancement of machine learning models by preparing and validating training data. It features machine learning (ML) model pre-labeling and customizable tagging, enabling efficient labeling of a variety of data types, including images, audio, text, time series, and video. This comprehensive tool supports an array of tasks, such as image classification, object detection, audio transcription, sentiment analysis, and optical character recognition, making it ideal for applications in natural language processing (NLP), computer vision, and Internet of Things (IoT) devices. Label Studio also provides configurable layouts and integrations with ML/AI pipelines and cloud storage. Pre-labeling requires imported predictions or a configured ML backend, and annotations need human review; model accuracy gains depend on data quality and separate evaluation.
Key features & benefits	✔️ Machine learning model pre-labeling through imported predictions or a connected backend. ✔️ Customizable tagging capabilities. ✔️ Supports multiple data types: images, audio, text, time series, and video. ✔️ Facilitates various tasks: image classification, object detection, audio transcription, sentiment analysis, optical character recognition. ✔️ Offers configurable layouts and integration with ML/AI pipelines.
Use cases and applications	Leverage customizable tagging and ML model pre-labeling features to streamline labeling of large datasets for image classification, to support model training and evaluation. Utilize versatile data labeling to annotate images with bounding boxes, polygons, or key points, to prepare training and evaluation data for computer-vision models. Enhance audio transcription tasks with support for labeling audio data, with human checks before using annotations to train speech-recognition models.
Who uses?	Machine Learning Engineers AI Researchers Data Scientists Data Analysts NLP Practitioners Computer Vision Engineers IoT Developers
Pricing	Community Edition: Free, open-source software under Apache 2.0 for self-hosting. Hosting, storage and connected-model services may incur separate costs. Hosted Starter: The official card displays $99 per month, plus $49 per month for each additional user, for teams of up to 12 users. Enterprise: Custom pricing by quote, with edition-specific workflow, deployment and security features. Checked October 24, 2026. These are not the earlier unverified Basic $10/Premium $20 entries. The comparison header separately uses a per-user label; confirm the included-seat allowance and order terms rather than inferring it from the card. No annual discount, tax total or ISO currency is assumed from the displayed dollar sign. See current pricing.
Tags	Data Labeling, Machine Learning, Data Annotation, AI Model Validation, NLP, Computer Vision.
App available?	Browser-based labeling interface. Community Edition can be installed and self-hosted, including through Python or Docker; hosted options are also available. Check the installation and browser requirements for your environment.

Sources: edition comparison, ML integration guide, installation guide and Apache 2.0 licence.

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