Zencastr, a prominent podcast platform, has significantly enhanced its transcription capabilities by leveraging Modal's scalable GPU platform, which allows them to efficiently process extensive audio data. Initially, Zencastr faced challenges with managing GPU infrastructure on Kubernetes, which was costly and required constant maintenance due to the various machine learning models and dependencies involved. By transitioning to Modal, they achieved cost-effective, dynamic scaling and seamless management of remote environments, allowing them to focus on AI development without significant overhead. This shift enabled tasks that previously took days to be completed in hours, optimizing their AI-powered transcription, speaker counting, laughter detection, audio quality scoring, and post-production enhancements. The use of Modal has also allowed Zencastr to scale batch audio processing to 1,500 concurrent GPUs efficiently, marking a major advancement in their infrastructure and development strategy, ultimately supporting their vision for the future of AI-enabled podcasting.