Seamless computational bio at Chai Discovery

Chai Discovery, a machine-learning drug discovery company, uses Modal to support flexible, large-scale computational workflows for designing medicines across biological targets and modalities. Its pipelines combine heterogeneous models, large biological datasets, and GPU-intensive tasks that can rapidly expand from small experiments to thousands of inference jobs, making traditional cloud infrastructure costly and operationally complex. Modal provides reproducible execution environments, shared persistent storage through Modal Volumes for large multiple sequence alignment datasets, and elastic GPU scaling that allows Chai to launch hundreds of GPUs within minutes and scale down when demand subsides. By avoiding repeated data downloads, hardware inconsistencies, manual cluster management, and infrastructure rewrites, Chai can use the same Python-based workflows from early research through production, accelerating experimentation and allowing scientists to focus more directly on molecular discovery.

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