Building ultra-low-latency voice AI applications on AWS
According to AWS. Learn how Cartesia uses purpose-built State Space Models and AWS infrastructure to deliver real-time voice AI capabilities globally.
- • Cartesia uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text.
- • Sonic delivers first audio byte in approximately 90ms with ultra-realistic voice quality across over 44 languages.
- • Ink provides native turn detection, context-aware end pointing, and streaming transcription for conversational AI.
- • Sonic is delivered as an Amazon SageMaker AI model package through AWS Marketplace for deployment within a VPC.
The Rise of Conversational AI
Conversational AI is transforming how humans interact with technology across virtual assistants, AI-powered contact centers, real-time translation, and accessibility tools. Delivering a truly natural voice experience remains one of the hardest challenges in AI infrastructure, as excess latency can break conversational flow and erode user trust.
State Space Models for Audio
Traditional transformer-based text-to-speech models struggle with streaming latency because they process entire sequences before producing output. State Space Models resolve this trade-off by treating sequences as systems that evolve continuously over time, maintaining a constant memory footprint and supporting native streaming.
Deploying Cartesia on AWS
You can deploy Cartesia models directly into your own AWS account for data sovereignty, latency control, or compliance. Sonic is delivered as an Amazon SageMaker AI model package through AWS Marketplace, deploying to a SageMaker AI endpoint running entirely within your virtual private cloud.
- 1. aws.amazon.comOpen source