Case Study: Handshake Scales Reinforcement Learning Environments with Daytona

Handshake saves 120 engineering hours monthly with Daytona

Handshake, a career network for the AI economy, faced a challenge scaling its reinforcement learning environment (RLE) platform. Their local infrastructure could not handle customer demand to provision thousands of secure, isolated sandboxes within hours for training AI models. They needed a solution that could provide enterprise-grade security, speed, and scale without requiring extensive engineering resources to build in-house, which led them to partner with Daytona.

By implementing Daytona's sandbox infrastructure, Handshake gained the ability to instantly provision millions of secure sandboxes monthly at scale. The solution provided critical features like Docker in Docker support, snapshots for reproducibility, and SSH access. This partnership saved Handshake 120 engineering hours per month and enabled them to successfully launch and scale their RLE platform, supporting over 1,000 Docker images and unpredictable customer demand.

Handshake