CVAT vs SuperAnnotate

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.

SuperAnnotate is infrastructure for AI teams building, evaluating, and improving datasets until they are ready for training. It handles RL environments, RLHF, image, video, audio, LiDAR, and custom modalities. Model-assisted workflows can pre-label data, judge rubrics, score pass rates, and route items between models and human experts. Teams can configure workflows, roles, quality gates, dashboards, and project or contributor analytics. Annotation types include classification, key points, bounding boxes, polygons, polylines, tiled imagery, cuboids, and skeletons. The platform offers a Python SDK and API, team collaboration, and integrations with AWS, Google Cloud, Azure, Snowflake, Databricks, and custom storage. External storage connections are read-only and do not automatically import new files. It runs through web, macOS, Windows, and API access. SuperAnnotate was founded in 2018 and is headquartered in Silicon Valley. Pricing is paid and available on request.