Building ultra-low-latency voice AI applications on AWS
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This article explains how to build ultra-low-latency voice AI applications on AWS using Cartesia’s State Space Models (SSMs). Unlike traditional transformers, SSMs offer constant memory footprints and native streaming support, enabling sub-100ms latency for both text-to-speech and speech-to-text tasks. Cartesia provides two core models: Sonic for high-quality, real-time speech synthesis and Ink for streaming transcription with intelligent turn detection. Developers can deploy these models via Amazon SageMaker AI for a managed, in-VPC experience or self-manage them on EC2 GPU instances for greater control over infrastructure and networking. The integration is particularly valuable for industries like contact centers, telehealth, and gaming, where natural conversational flow is critical. By leveraging AWS infrastructure, organizations can maintain data sovereignty while achieving human-like responsiveness in their voice interfaces. The post outlines specific deployment steps, prerequisites, and scaling strategies for both managed and self-managed options.
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