Introducing Modal Batch: Process 1 million jobs with 1 line of code

Modal Batch is a new job-processing interface and durable queue system designed to run large-scale, fault-tolerant batch workloads such as embedding documents, preprocessing audio, and preparing training data. Users define a Python Modal Function with optional hardware, image, retry, and storage settings, then use `.spawn_map` to distribute up to one million inputs across thousands of cloud containers, including GPU-enabled instances, with execution guaranteed for up to seven days. Compared with Modal’s earlier `.map` and `.spawn` methods, the service increases queue capacity 500-fold, simplifies concurrent job submission, and provides logs and metrics for individual inputs and aggregate jobs. Modal positions the product as an alternative to managing distributed infrastructure and orchestration systems, highlighting customers including Harvey, which reported a tenfold document-processing speedup, Suno, which uses it to scale GPU audio preprocessing, and Achira, which relies on batching and retries for scientific dataset preparation. Planned additions include function caching, programmatic job controls, and SDK support for JavaScript and other languages.

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