The best Keylabs alternatives
The rest of Best AI Image Annotation Tools, in this site's order: each with what it costs, where it runs and its record score.
Roboflow is intended for developers and enterprises building computer vision applications.
It suits teams labeling varied dataset types or connecting labeling workflows to machine-learning backends.
VIA may suit researchers, academic projects, and commercial applications that need manual annotation of images, audio, or video.
COCO Annotator suits teams creating image datasets for localization or object detection, particularly those able to run a Docker-based service.
It suits people preparing labeled image datasets for small computer vision projects.
CVAT suits individuals and teams preparing labeled datasets for AI work, particularly those needing review workflows, storage integrations, or…
The Community plan is described for open-source projects, individuals, ML researchers, and small teams.
Pixano is suited to AI developers and teams working with computer vision datasets, including applications in manufacturing, security, robotics, or…
Segments.ai is aimed at computer vision engineers and data scientists working in autonomous robotics and driving.
Prodigy suits developers and data teams building labeled datasets for machine-learning systems.
It suits researchers and computer vision teams who need image annotations in polygon, scribble, or mask form.
It suits developers and computer-vision annotators preparing YOLO training data on Windows, Linux, or Apple Silicon Macs.
It suits AI labs and enterprises building training datasets, evaluations, reinforcement learning environments, or agent workflows.
It suits teams preparing varied datasets for AI models, especially those needing structured reviews, model-assisted labeling, or cloud integrations.
Dataloop suits data engineers, data scientists, software engineers, AI leaders, and human reviewers building collaborative AI workflows.
It suits AI teams managing multimodal annotation, evaluation, and data operations.
It suits ML research teams, startups, engineers, and researchers iterating on experimental models or labeling instructions.
It may suit computer vision teams working with image or video datasets, particularly those needing on-premise or air-gapped deployment.