Neural machine translation (NMT) is often heralded as the most effective approach to machine translation due to its success on language pairs with large parallel corpora. However, neural methods produce less than ideal results on low-resource languages when their performance is evaluated using accuracy metrics like the Bilingual Evaluation Understudy (BLEU) score. One alternative to NMT is rule-based machine-translation (RBMT), but it too has drawbacks. Furthermore, little research has been done to compare the two approaches on criteria beyond their respective accuracies. This thesis evaluates RBMT and NMT systems holistically based on efficacy, ethicality, and utility to low-resource language communities. Using the language Karachay-Balkar as a case-study, the latter half of this thesis investigates how two free and open-source machine translation packages, Apertium (rule-based) and JoeyNMT (neural), might support community-driven machine translation development. While neither platform is found to be ideal, this thesis finds that the Apertium is more conducive to a community driven machine translation development process than JoeyNMT when evaluated on the criteria of efficiency, accessibility, ease of deployment, and interpretability.