  We present EzGal, a flexible python program designed to easily generate
observable parameters (magnitudes, colors, mass-to-light ratios) for any
stellar population synthesis (SPS) model. As has been demonstrated by various
authors, the choice of input SPS models can be a significant source of
systematic uncertainty. A key strength of EzGal is that it enables simple,
direct comparison of different models sets. EzGal is also capable of generating
composite stellar population models (CSPs) and can interpolate between
metallicities for a given model set. We have created a web interface to run
EzGal and generate observables for a variety of star formation histories and
model sets. We make many commonly used SPS models available from this
interface; the BC03 models, an updated version of these models, the Maraston
models, the BaSTI models, and finally the FSPS models. We use EzGal to compare
magnitude predictions for the model sets as a function of wavelength, age,
metallicity, and star formation history. We recover the well-known result that
the models agree best in the optical for old, solar metallicity models, with
differences at the ~0.1 magnitude level. The most problematic regime for SPS
modeling is for young ages (<2 Gyrs) and long wavelengths (lambda >7500
Angstroms) where scatter between models can vary from 0.3 mags (Sloan i) to 0.7
mags (Ks). We find that these differences are best understood as general
uncertainties in SPS modeling. Finally we explore a more physically motivated
example by generating CSPs with a star formation history matching the global
star formation history of the universe. We demonstrate that the wavelength and
age dependence of SPS model uncertainty translates into a redshift dependent
model uncertainty, highlighting the importance of a quantitative understanding
of model differences when comparing observations to models as a function of
redshift.
