Automatic Speech Recognition (ASR) and Speech-to-Text (STT) are distinct technologies that handle audio input differently to serve various business needs. ASR focuses on converting raw audio into unpunctuated text for machine processing, prioritizing speed and accuracy for real-time applications like voice commands and call routing. In contrast, STT transforms audio into formatted text with punctuation and speaker labels, making it suitable for legal documentation, accessibility, and compliance purposes. The choice between ASR and STT hinges on specific use cases, such as the need for real-time intent detection with ASR or the requirement for human-readable output with STT. Industries like contact centers, healthcare, media, and accessibility utilize these technologies differently, based on their operational requirements and constraints. Deepgram offers solutions that integrate both ASR and STT into a unified platform, enabling seamless deployment of voice agents and compliance systems while ensuring high accuracy, low latency, and scalability in production environments.