Best Open-Source Data Labeling Tools in 2025
By Daniel Clarke
If you need to label training data without vendor lock-in or recurring costs, open-source tools are the obvious choice. The two dominant options are CVAT and Label Studio — both free to self-host, both backed by large communities, and both capable of production-scale annotation work.
Here's how to choose between them.
CVAT: Best for Computer Vision
CVAT is the go-to open-source annotation tool for computer vision. Backed by the OpenCV Foundation, it has 14K+ GitHub stars and a 4.8/5 rating on G2 — the highest of any open-source annotation tool.
Strengths:
- Native 3D point cloud and LiDAR annotation (rare in open-source tools)
- 19 export formats including YOLO, COCO, PASCAL VOC, and KITTI
- Built-in auto-annotation with AI models (SAM 2/3 integration)
- Cloud storage integration for S3, GCP, and Azure
Limitations:
- Computer vision only — no text, audio, or document support
- Performance degrades with very large video files
- Self-hosted deployment requires technical expertise
Pricing: Free to self-host. Cloud plans start at $23/month, but the free cloud tier is limited (1 project, 3 tasks, 1GB storage).
Choose CVAT if: You're building computer vision models and want the deepest export format support without paying enterprise prices.
Label Studio: Best for Multi-Modal Data
Label Studio is the most flexible open-source labeling platform, supporting images, video, text, audio, time series, and PDFs in a single tool. With 24K+ GitHub stars and over 1 million users, it's the largest open-source annotation community.
Strengths:
- Broadest data type support of any open-source tool
- Highly customizable XML-based labeling templates
- LLM evaluation and RLHF workflow support
- Strong Python SDK for pipeline integration
Limitations:
- Steep learning curve — XML templates require effort to master
- Performance issues with very large datasets
- Role-based workflows only in the paid Enterprise edition
Pricing: Free to self-host via pip, brew, or Docker. Cloud plans start at $50/month.
Choose Label Studio if: You need to annotate multiple data types (text + images, audio + transcription, etc.) or you're doing LLM evaluation work.
Other Options Worth Considering
Supervisely offers a free Community tier with built-in AI models (SAM2, YOLO v11) and strong support for DICOM medical imaging. It's not fully open-source, but the free tier is functional for small projects and researchers.
Roboflow isn't open-source, but its freemium tier includes $60/month in credits with excellent AI-assisted labeling. The catch: your data becomes open source on Roboflow Universe. If you're comfortable with public data, it's the fastest way to go from raw images to deployed model.
Quick Comparison Table
| Tool | Data Types | Self-Host | GitHub Stars | Best For |
|---|---|---|---|---|
| CVAT | Images, video, 3D/LiDAR | Yes (free) | 14K+ | Computer vision |
| Label Studio | Images, video, text, audio, PDFs | Yes (free) | 24K+ | Multi-modal, LLMs |
| Supervisely | Images, video, 3D, DICOM | Yes (paid) | — | Medical imaging |
| Roboflow | Images, video | No | — | Quick CV projects |
Deployment Considerations
Both CVAT and Label Studio can be self-hosted via Docker, which gives you full control over your data. Self-hosting is free but requires:
- Server infrastructure (cloud VM or on-premise)
- Technical expertise to maintain and update
- Your own backup and security setup
If you don't have DevOps capacity, both offer managed cloud options. CVAT's cloud starts cheaper ($23/month vs $50/month for Label Studio), but Label Studio's free self-hosted version is arguably more powerful out of the box.
Bottom Line
CVAT for pure computer vision work with advanced export needs. Label Studio for multi-modal data or LLM workflows. Both are production-ready, both are genuinely free to self-host, and both avoid the vendor lock-in that comes with enterprise platforms.
If you're evaluating enterprise options instead, see our comparisons of Appen, Scale AI, and Labelbox.