LabelStudio

Label Studio is an open-source platform for data labeling and AI evaluation, designed for teams that need to prepare high-quality training data and evaluate AI models across computer vision, NLP, audio, time series, and multi-modal data. It is built for data scientists, ML engineers, and AI teams who want full control over their annotation pipelines and human-in-the-loop workflows. With customizable labeling interfaces, you can adapt templates to your specific data types and tasks, from image classification and object detection to named entity recognition, audio transcription, and event labeling in time series. A standout feature is the Vibe Code agentic builder, which lets you generate labeling UIs from natural language descriptions, complete with version history and rollback. For video, the new VideoVector tag powered by SAM 2 enables object segmentation and tracking across frames, eliminating tedious frame-by-frame annotation. Beyond labeling, Label Studio supports AI evaluation: LLM rubric scoring, side-by-side comparisons, RAG QA retrieval relevance grading, RLHF preference collection, and agentic trace review—making it a one-stop shop for both training data and model assessment. Integration is pipeline-first: API, Python SDK, and webhooks allow you to create projects, stream predictions, and trigger active learning or evaluation workflows in real time. Data can be synced from cloud storage like S3, GCS, and Azure Blob Storage, and you can connect any model for AI-assisted pre-labeling. The tool can be self-hosted via Docker, Kubernetes, or pip, or used as Label Studio Enterprise with SSO, RBAC, and SLAs. With over one million practitioners, Label Studio is the most flexible open-source option, but it requires technical setup. Compared to alternatives like Supervisely or Dataloop, it offers more control and customization, though not a fully managed zero-ops experience.