End-to-end computer vision for data annotation, YOLO training, deployment, and monitoring
Ultralytics Platform is an end-to-end computer vision platform for dataset annotation, model training, and deployment. The site positions it as a single workflow for building vision systems with Ultralytics YOLO models.
The platform brings together browser-based annotation, cloud training, deployment, and monitoring. It supports common computer vision tasks such as detection, instance segmentation, semantic segmentation, classification, pose estimation, and oriented bounding boxes, with exports for edge, cloud, mobile, and embedded environments.
The pricing page shows three plans: Free, Pro, and Enterprise. Free is aimed at personal, research, or open-source work, Pro is for professionals and small teams, and Enterprise adds features such as SSO/SAML, RBAC, and on-premise deployment options.
Label images and videos with bounding boxes, polygons, semantic masks, keypoints, and oriented bounding boxes across 6 annotation tasks. The platform also includes SAM-powered smart annotation, team review, and versioning.
Launch cloud training jobs with one click, choose from 22 GPU configurations, and watch live metrics during training. The platform supports Ultralytics YOLOv5, YOLOv8, YOLO11, and YOLO26, and can be used through a no-code interface or a Python SDK.
Deploy models to 43 global regions with dedicated endpoints, auto-scaling, and built-in monitoring. Endpoints can scale to zero when idle and be configured with CPU and memory limits.
Export trained models to 19 optimized formats for edge, cloud, mobile, and embedded use. The site specifically names ONNX, TensorRT, CoreML, TFLite, and QNN among the supported outputs.
Use the built-in Predict tab to test trained models in the browser. Users can upload an image or open the camera and adjust confidence, IoU, and image size to inspect predictions in real time.
Work with the platform through generated Python, JavaScript, and cURL examples, plus a REST API for automation and CI/CD workflows. The pricing page also shows a monitoring dashboard with performance, drift, and inference metrics.
Teams labeling new datasets can use the annotation tools to draw bounding boxes, polygons, masks, keypoints, or oriented bounding boxes, then review and version the work before training starts.
Developers training detection, segmentation, classification, pose, or OBB models can launch cloud jobs, choose GPU configurations, compare experiments, and monitor metrics as the model learns.
Product teams moving a model into production can deploy to a regional endpoint, connect via Python, JavaScript, or cURL, and watch request volume, latency, and errors from the dashboard.
Teams that need portability can export a model to formats such as ONNX, TensorRT, CoreML, TFLite, or QNN for edge devices, mobile apps, or embedded systems.
Organizations evaluating fit or upgrading plans can start with Free, move to Pro for team collaboration and higher usage limits, or discuss Enterprise for SSO, RBAC, and dedicated support.
Ultralytics Platform is a computer vision platform for annotating datasets, training YOLO models, and deploying them as inference endpoints. The source pages show browser-based annotation, cloud training, and deployment to 43 global regions.
The source shows support for 6 annotation tasks, 22 cloud GPU configurations on the training page, and deployment to 43 global regions with 19 export formats.
The platform supports browser inference and deployment endpoints, and the pricing page includes a REST API plus generated code examples for Python, JavaScript, and cURL.
The pricing page shows Free, Pro, and Enterprise plans. Free is listed for personal, research, or open-source projects, Pro is for professionals and small teams, and Enterprise adds options such as SSO/SAML, RBAC, on-premise deployment, and dedicated support.
The source does not list third-party integrations in detail. It does show compatibility with common annotation formats such as YOLO, COCO, and VOC, plus export formats including ONNX, TensorRT, CoreML, TFLite, and QNN.
Traffic data is for reference only.
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