Robust Bayesian model comparison and tension quantification are essential for interpreting the wealth of modern cosmological data, yet they remain computationally prohibitive bottlenecks. High-dimensional nested sampling runs often require thousands of CPU hours, limiting the community’s ability to explore model extensions or validate new datasets rapidly. To address this, I present unimpeded [2511.04661], a new public resource designed to democratise access to these expensive calculations. Acting as a “Planck Legacy Archive” for nested sampling, unimpeded provides a massive grid of pre-computed chains covering 8 cosmological models (including $\Lambda$CDM and extensions) across 69 observational datasets. The accompanying Python package transforms what used to be months of supercomputer time into seconds on a laptop, enabling instant access to Bayesian evidences, parameter estimates, and robust tension metrics. I will demonstrate the power of this framework by applying it to the recent controversy surrounding DESI DR2 and evolving dark energy [2511.10631]. While frequentist approximations have suggested a preference for dynamic dark energy (w0waCDM), our direct Bayesian analysis reveals that the combination of DESI BAO and Planck CMB actually favours the simpler LambdaCDM model. Using unimpeded to dissect this result, we show that the apparent preference for evolving dark energy is driven primarily by resolving a statistical tension between DESI and the DES-Y5 supernova catalogue, rather than an intrinsic signal within the BAO data itself, warranting a cautious interpretation of its statistical significance.