Compare Runcrate vs. supercomputer

Runcrate offers AI teams and their agents a unified account to access GPUs and inference capabilities seamlessly. Users can rent various GPU models, including H100, H200, A100, L40S, RTX 4090, and RTX PRO 6000, with over 20 options available, for hourly use, featuring SSH, Jupyter, and VS Code directly in their browsers. A launch can be initiated in approximately four minutes, and users are only charged for running time, with most GPUs billing by the second. In terms of inference, clients can access more than 200 open models, such as DeepSeek, Qwen, Llama, Kimi, FLUX, and Whisper, through a single OpenAI-compatible API, ensuring compatibility with existing clients. Pricing is structured based on usage metrics like tokens, images, or seconds, while storage volumes allow for the retention of datasets and checkpoints across different instances. Reserved clusters can support over eight GPUs or provide month-long service options. Designed specifically for agents, users can connect tools like Claude Code, Codex, or Cursor to the Runcrate MCP server using a single command and sign in via their browser without needing to input any keys. Once connected, agents can easily launch a GPU, execute commands, and shut it down when finished. Additionally, the platform provides a command-line interface, which can be installed with a simple command, along with SDKs available for both Python and TypeScript to enhance development flexibility.

Supercomputer is a cloud-based computational service that offers both GPU and CPU sessions designed for machine-learning tasks and other resource-heavy operations. It enables developers, researchers, and teams to initiate and oversee sessions through a command-line interface or Python SDK, allowing them to run their code on customizable GPU instances while maintaining their workspace files across sessions. This service facilitates various activities such as model training, fine-tuning, inference, experiments, evaluations, batch processing, hyperparameter optimization, and data-processing tasks. Users have the ability to upload their scripts and datasets, perform commands in persistent sessions, and either retrieve outcomes or save them in shared volumes. Notably, compute charges are incurred only when sessions are active, and any inactive sessions keep their workspace available for future use. The pricing operates on a system of prepaid credits, calculating charges by the second, with variations in rates depending on the chosen GPU configuration. Additionally, this flexibility ensures that users can optimize both their workflows and costs effectively.