Supervisely

Vision MLOps end-to-end. Annotate, train, deploy.

Annotation tooling, auto-labelling, hosted training, and edge deployment for computer-vision projects. Strong default when you're shipping a custom vision model rather than reaching for a multimodal LLM.

Data platform to curate, label, and manage AI training data.

An enterprise data development platform for preparing high-quality training data across images, video, documents, audio, DICOM, and 3D point clouds. It pairs AI-assisted labeling (SAM auto-segmentation, object tracking) with data curation, model evaluation, and workflow tooling, plus LLM-powered data agents for document tasks. Used heavily in medical imaging, robotics, and other physical-AI domains.

Multi-type data labeling and AI evaluation across every modality.

Widely-used open-source tool for labeling and annotating data across images, text, audio, video, and time-series, with a standardized export format for training and fine-tuning. ML backends can pre-label data to speed up human review, and it increasingly doubles as a human-in-the-loop AI evaluation surface. Maintained by HumanSignal, which offers a hosted Starter tier and Label Studio Enterprise.

FiftyOne — open-source vision data platform.

A toolkit for exploring, debugging, and curating vision datasets. Strong story for finding model failure modes, balancing classes, and tracking experiment drift across visual data at scale.