Compressive sensing (CS) is a signal acquisition strategy that, based on the assumption of sparsity, promises to relax the design constraints of signal acquisition systems with respect to conventional strategies. In this paper, we contrast signal acquisition systems for low-rate applications based on analog CS encoding with systems based on digital CS encoding. We consider the complete signal chain from acquisition to reconstruction, with particular attention to the effects of quantization, and show that the two schemes differ significantly in encoder precision, measurement resolution, compression ratio, and reconstruction quality. Further, we develop first-order power estimation models to asses the relative energy-efficiency of different CS and conventional signal acquisition systems. Our numerical evaluations suggest that when the power consumption of data storage/communication outweighs the power consumption of data acquisition and processing, analog CS systems can outperform their digital counterparts, despite their higher hardware complexity. Moreover, we provide evidence that the common special case of analog and digital encoding, known as non-uniform sampler, performs best under all conditions.