Voice Activity Detection (VAD) is a crucial technology in modern voice applications, designed to distinguish between speech and non-speech audio frames, thereby improving efficiency in processing audio data. It operates through a four-stage pipeline: frame segmentation, feature extraction, classification, and post-processing, which together enable reliable detection by balancing latency, compute costs, and accuracy. Different VAD algorithms, such as energy-based, spectral variants, statistical models, and machine learning approaches, are chosen based on the specific acoustic environment and business needs. VAD significantly reduces bandwidth and compute costs in applications like automatic speech recognition (ASR) pre-processing, predictive dialers, and clinical dictation by eliminating non-essential audio data. The performance of VAD systems is measured using metrics such as precision, recall, and F1 score, alongside subjective evaluations to ensure user experience is maintained. Deepgram's advanced VAD solutions offer high accuracy in challenging noise conditions and are used in various enterprise applications to enhance speech recognition and processing capabilities.