This study conducts a head-to-head techno-economic comparison of vertical profiling Light Detection and Ranging (LiDAR) and fixed-wing Unmanned Aerial Vehicles (UAVs) deployments for wind resources assessment at a representative 120 MW onshore project in flow‑distorting terrain in Germany. A simplified energy yield model and a Monte Carlo framework propagate literature-based measurement and flow model uncertainties through different measurement scenarios to the exceedance levels (P50, P90) of Annual Energy Production (AEP), Net Present Value (NPV) and Levelized Cost of Electricity (LCOE). The analysis shows that both technologies achieve similar point measurement uncertainties (±0.4–0.5 m/s, ±7–10°). The dominant lever for reducing AEP uncertainty and improving P90 is increased direct spatial coverage, not marginal gains in instrument accuracy. Under consistent cost and financing assumptions, UAV-based deployments deliver the largest P90 uplift, higher NPV and lower LCOE than LiDAR configurations.