Case Study: Vmax achieves 1,000+ parallel sandboxes and faster AI research with Daytona

Vmax launches 1,000+ parallel sandboxes in one minute with Daytona

Vmax, an AI research and development company, faced a significant challenge in scaling its research into open-ended learning. Their work required provisioning thousands of concurrent, isolated sandboxes to allow AI agents to generate and validate their own tasks. Their in-house system could not meet the demand for speed and scale, creating bottlenecks that threatened to halt their research progress. They turned to Daytona to find a dedicated runtime platform solution.

By implementing Daytona, Vmax gained access to a platform that could instantly provision over 1,000 parallel sandboxes on demand with airtight isolation. This solution saved an estimated 30 days of engineering time and eliminated infrastructure bottlenecks. With Daytona, Vmax can now keep its research pipeline moving continuously, allowing its team to focus on core experiments and accelerate the development of autonomous AI agents.

Vmax