Ray Summit stories showcase how users are using Ray to scale various applications, including training large language models, building ML platforms, and scaling parallel Python jobs. Instacart migrated from an AWS ECS stack to Ray, achieving a 10x better cost performance and simplifying deployment. Dow used Ray's multi-agent decomposition approach to solve a complex production schedule design problem, reducing computation time by 10x. Ridecell leverages Ray Tune to efficiently search across hyperparameters for their deep learning models, optimizing trials and preventing costly mistakes. These examples demonstrate the power of Ray in scaling applications and solving complex problems.