Train vision AI models in clicks, not days

Yes. Ultralytics Platform supports local training on your own GPUs or CPUs. Install the Ultralytics Python package, set your API key, and start training. Real-time metrics stream directly to the platform dashboard alongside your cloud training runs. This gives you the flexibility to use your own hardware while keeping all experiments organized in one place.

Ultralytics Platform offers 22 GPU options ranging from $0.24 to $4.99 per hour. For most workloads, the RTX PRO 6000 (96 GB, $1.89/hr) is a strong default. For time-sensitive training, the H100 and H200 deliver maximum performance. For testing and small datasets, budget options like the RTX 2000 Ada ($0.24/hr) work well. The platform shows an estimated cost and duration before you start, so you can choose the right balance of speed and budget for your project.

If a training run fails, you won't be charged. You're only billed for actual GPU time on completed or manually canceled runs. Checkpoints are saved throughout training, so if a run is interrupted or canceled, your progress up to that point is preserved. You can review console logs to diagnose issues and restart training with adjusted settings.

Yes. Ultralytics Platform supports concurrent training runs. Free plan users can run up to 3 simultaneous training jobs, Pro users can run up to 10, and Enterprise users can run unlimited concurrent jobs. Each run gets its own dedicated GPU instance.

Training time depends on your dataset size, model size, number of epochs, and GPU selection. As a reference, training YOLO26n on 1,000 images for 100 epochs takes approximately 2 to 3 hours on an RTX PRO 6000. Larger models like YOLO26x will take longer for the same configuration. The platform estimates cost and duration before training starts, so you always know what to expect.

Model training is the process of teaching a computer vision model to recognize patterns in visual data. During training, the model processes thousands of labeled images, adjusts its parameters, and progressively improves its ability to detect, segment, or classify objects. On Ultralytics Platform, training is integrated directly into the annotation and deployment workflow. Once your dataset is labeled, you can select a YOLO model, choose a cloud GPU, and start training without leaving the platform.