How Zencastr transcribed hundreds of years of audio in days with Modal

Zencastr moved its podcast transcription workloads to Modal's GPU platform, scaling to 1,500 concurrent GPUs within minutes to process hundreds of years of audio in just a few days. The shift from self-managed Kubernetes GPU nodes to Modal's scale-to-zero infrastructure cut idle GPU costs and removed driver and dependency management overhead. All core ML workloads now run on Modal, including transcription, speaker counting, laughter detection, and audio quality scoring.