Climate change and other factors are increasing risks and uncertainties around fuels and fire management. As land management agencies seek to use prescribed fire and fuel treatments to shift ecosystems back into balance and protect communities at broader scales, there is a need for detailed fuels data and modeling systems that help managers explore trade-offs between different alternatives and which highlight potential benefits and risks. Here, we describe FastFuels, a 3D fuel modeling platform which provides seamless input data for advanced fire models such as QUIC-Fire and FDS. The default or baseline approach leverages forest inventory databases and statistical imputation to provide representative data for anywhere in the contiguous United States. But when other data, such as airborne LiDAR scanning (ALS) is available, FastFuels has data assimilation pathways called “onramps” to refine maps with more site-specific data. FastFuels can be used to simulate fuel treatments, and with the QUIC-Fire model, simulate prescribed burns. A growing suite of tools provides great flexibility in examining impacts of fuel treatments or disturbance-driven fuels changes. We describe the overall system design and architecture, demonstrate use of the ALS onramp with LiDAR data provided by the Confederated Salish and Kootenai Tribes (CSKT) and examine different scenarios around some real-world prescribed burns. We conclude with discussion of planned future developments. We hope that these evolving systems will empower managers to better understand nuances, tradeoffs and risks in prescribed fire and fuel management.