Ecological forecasting challenges have emerged as an effective mechanism for advancing predictive capacity in ecology by coordinating community efforts to generate, evaluate, and refine near‐term forecasts of yet-to-be-collected data. The NEON Ecological Forecasting Challenge exemplifies this approach, having processed more than 82 million individual forecasts across 81 sites since 2021. We present the design principles, implementation, and lessons learned from the cyberinfrastructure developed to support the NEON Challenge, with an emphasis on its applicability to future forecasting initiatives. The platform is a serverless, open‐source system that automates the core forecasting workflow, including target data generation, probabilistic forecast ingestion and validation, evaluation using proper scoring rules, and dissemination of results. The infrastructure relies on cloud‐native, vendor‐agnostic technologies, including object‐based storage, continuous integration workflows, and containerized execution, to ensure scalability, reproducibility, and long‐term maintainability. Its modular architecture facilitates adaptation to new forecasting targets, data sources, and observatories, enabling rapid deployment for emerging ecological forecasting challenges.

Keywords: NEON, Challenges, cyberinfrastructure