We present the snapshot distance method (SDM), a modern incarnation of a proposed technique for estimating the distance to a Type Ia supernova (SN Ia) from minimal observations. Our method, which has become possible owing to recent work in the application of deep learning to SN Ia spectra (we use the deepSIP package), allows us to estimate the distance to an SN Ia from a single optical spectrum and epoch of $2+$ passband photometry -- one nights worth of observations (though contemporaneity is not a requirement). Using a compilation of well-observed SNe Ia, we generate snapshot distances across a wide range of spectral and photometric phases, light-curve shapes, photometric passband combinations, and spectrum signal-to-noise ratios. By comparing these estimates to the corresponding distances derived from fitting all available photometry for each object, we demonstrate that our method is robust to the relative temporal sampling of the provided spectroscopic and photometric information, and to a broad range of light-curve shapes that lie within the domain of standard width-luminosity relations. Indeed, the median residual (and asymmetric scatter) between SDM distances derived from two-passband photometry and conventional light-curve-derived distances that utilise all available photometry is $0.013_{-0.143}^{+0.154}$ mag. Moreover, we find that the time of maximum brightness and light-curve shape (both of which are spectroscopically derived in our method) are only minimally responsible for the observed scatter. In a companion paper, we apply the SDM to a large number of sparsely observed SNe Ia as part of a cosmological study.
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