Case Study: Stanford University accelerates sandbox provisioning with Daytona

Stanford University provisions sandboxes 3x faster with Daytona

Stanford University's Computer Science Department, specifically a research group working on open-source AI agent development, faced significant challenges provisioning scalable, customized sandbox infrastructure for machine learning experiments. Their need for fast, stateful, and long-running sandboxes to train agents was constrained by time, resource limitations, and the high engineering cost of building an in-house solution, which was estimated to take four months. They turned to vendor Daytona for its managed runtime platform.

Daytona provided a scalable runtime solution that automated the sandbox lifecycle, allowing the team to programmatically create highly customized environments with minimal overhead. The results included sandbox provisioning that was 3x faster, the ability to run over one million sandboxes simultaneously each month, and a time savings of four months on infrastructure engineering. Daytona's solution eliminated operational overhead and accelerated the team's research pipeline.

Stanford University