Runpod

RunPod focuses on rapid deployment of GPU instances, appealing to both researchers prototyping small models and developers training large AI workloads. It uses container-based technology, so you can easily port your Docker images or select from community-provided ML frameworks.

The platform’s web-based dashboard allows you to launch instances on-demand, and its documentation outlines an API for programmatic control. A built-in spot marketplace enables you to bid on spare GPU capacity at potentially lower rates, although these spot instances come with the risk of preemption. Persistent volume support is also covered in the docs, letting you store datasets or results between sessions. RunPod’s containerized approach simplifies environment setup, making it well-suited for machine learning use cases. If you’re integrating GPU compute into a continuous integration (CI) pipeline, you can spin up new pods automatically, train or test your models, and shut them down once tasks are complete. This workflow helps minimize compute costs while maintaining development velocity.