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SPECIFICTION
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COCO Annotator vs CVAT

COCO Annotator

github.com

CVAT

cvat.ai

Side by side

FigureCOCO AnnotatorCVAT
Price fromFree$1.92/mo
Free planYesYes
Free trial——
Open sourceYes—
WebYesYes
WindowsYes—
LinuxYes—
APIYesYes
Self-hostedYesYes
Plans published28
Spec score · AI Image Annotation Tools9.0 (#2)8.4 (#5)
Annotation featuresIt supports object segmentation, keypoints, disconnected objects as one instance, multiple labels per image segment, and custom metadata.Its visual annotation tools include boxes, pixel masks, polygons, keypoints, skeletons, and object tracks.
DeploymentThe documentation provides production and development Docker builds, and describes the production build as stable and suitable for a large user base.CVAT is offered as a managed cloud service or as a self-hosted deployment, including on-premises and air-gapped options.
PurposeCOCO Annotator is a web-based image annotation tool for creating training data for image localization and object detection.CVAT turns raw images, video, 3D data, and audio into labeled datasets for AI workflows.
SupportThe project invites users to join its Discord community of machine-learning practitioners.Enterprise Basic includes 24-hour email support with a 24-hour response SLA and a private helpdesk channel for urgent critical issues.

Plans

COCO Annotator

  • MIT-licensed softwareFree
  • Open-source self-hostedFree

COCO Annotator pricing →

CVAT

  • CommunityFree
  • CVAT Online FreeFree
  • CVAT Online Solo Yearly$23 a year
  • CVAT Online Team Yearly$23 a year
  • CVAT Online Solo Monthly$33 a month
  • CVAT Online Team Monthly$33 a month
  • Enterprise Basic$12,000 a year
  • Enterprise PremiumPriced on request

CVAT pricing →

In their own words

COCO Annotator

COCO Annotator is a web-based tool for creating image annotations used in training data for localization and object detection. It supports bounding boxes, polygons, segmentation masks, keypoints, and points, along with disconnected shapes treated as one instance, multiple labels on an image segment, and custom metadata. Users can import existing COCO datasets and export annotations in COCO format. Assisted options include DEXTR, MaskRCNN, Magic Wand, semi-trained model annotation, and Google Images dataset generation. The software includes user authentication and a REST API, with a Swagger interface at localhost:5000/api. It is self-hosted on Linux or Windows, and Docker with docker-compose is required because Docker is the only supported installation method. Documentation covers development and production builds, centralized datasets, and external access. The server uses Flask, Eventlet, and Gunicorn, while long-running requests go to RabbitMQ workers. The project recommends HTTPS to encrypt browser-to-site communication and invites users to its Discord community. It is listed as free under an MIT-licensed plan.

Visit github.com

CVAT

CVAT turns images, video, 3D data and audio into labeled datasets for AI workflows. Its annotation tools include boxes, masks, polygons, keypoints, skeletons and object tracks, with additional listed types such as cuboids, ellipses and tags. Teams can review jobs through validation and acceptance stages and assess quality using ground truth, honeypots and consensus workflows. Storage connections include Amazon S3, Azure Blob Storage, Google Cloud Storage, compatible buckets and self-hosted storage. CVAT names SAM 2, SAM 3, Ultralytics, Hugging Face and custom models as labeling aids. It exports to more than 20 formats, including COCO, YOLO, KITTI, Cityscapes and Pascal VOC, and offers API, SDK and CLI automation. Deployment can be managed in the cloud or self-hosted, including on-premises and air-gapped options. Free plans are available; online paid plans start at $23/user/mo billed annually. CVAT lists security controls but says it does not hold ISO 27001 or SOC 2 certifications.

Visit cvat.ai