How Suno cut 4 months off its launch timeline with Modal

Suno used Modal to scale inference and batch pre-processing across thousands of GPUs, bringing its music generation model to market four months early instead of hiring engineers to build and maintain infrastructure. The team ran batch pre-processing with a few short Python scripts and no config files, then expanded to model deployment with HTTP-exposed functions and chained inference across multiple models and containers. Modal's auto-scaling to thousands of GPUs handled holiday demand spikes without long-term GPU reservations.