Running real-time applications on Modal: Low-Latency Voice Agents

Voice AI application development requires robust serverless infrastructure to manage fluctuating workloads and minimize latency. Core components—including speech-to-text, text-to-speech, voice activity detection, and diarization—perform most efficiently when deployed as independent, auto-scaling services. Optimizing performance involves batch processing with sorted audio durations to maximize GPU throughput and utilizing GPU snapshots to significantly reduce cold start times. Real-time voice agents, often constructed using frameworks like PipeCat or LiveKit, rely on WebRTC for low-latency communication. While end-to-end speech-to-speech models are emerging, modular pipelines remain superior for complex tasks requiring tool calling and precise context management. Balancing cost and performance necessitates strategic container management, such as maintaining warm lightweight front-end nodes while scaling heavy inference engines on demand to ensure responsive, cost-effective deployments.