Architecting Airflow infrastructure involves deciding between a monolithic or a multi-tenant setup, a choice driven by the limitations of Airflow's scheduler, which cannot be horizontally scaled unlike other components. Initially, Astronomer implemented a single massive Airflow deployment for managing various workflows, which proved unsustainable as the scheduler became a bottleneck due to its CPU-intensive nature and inability to handle silent failures effectively. Transitioning to multiple Airflow instances tailored to specific job requirements and using Kubernetes for container orchestration enhanced fault tolerance and availability, enabling quicker iterations and resource allocation adjustments. This polycentric design strategy, demonstrated by Metarouter's deployment of three separate Airflow environments, facilitates smoother operations and more efficient management of production and reporting tasks, although it acknowledges that there is no universal solution for all data infrastructure needs.