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Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Compound AI workflows are increasingly used to serve complex AI tasks by coordinating multiple AI models and software components. This approach enables deployment flexibility, as each workflow stage can expose different model variants and resource requirements, but it also expands the deployment choices. A deployment must choose an execution plan that selects AI models for each compound AI workflow stage and places them on a heterogeneous cluster in order to satisfy SLOs. Deployment optimizers therefore need estimates to compare many candidate plans and identify feasible ones. System metrics c

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