Production Voice AI on Amazon SageMaker

Voice AI in regulated industries forces a tradeoff most teams have not solved well. AWS-native voice services are easy to deploy but lag on accuracy and modern voice-agent patterns. Best-of-breed voice models often live outside the AWS perimeter, breaking compliance posture and adding cross-cloud latency.

That's why we wrote Production Voice AI on Amazon SageMaker, a reference guide for ML and AI platform leaders in financial services, healthcare, insurance, and other regulated verticals who need real-time voice AI inside their SageMaker environment. Deepgram is available natively on Amazon SageMaker AI as a real-time endpoint, with no custom pipelines and no orchestration glue. Voice data stays inside your SageMaker environment, and the deployment inherits the compliance posture of your AWS account.

Learn how to:

• Deploy Deepgram STT and TTS as SageMaker AI real-time endpoints using bidirectional streaming
• Run Deepgram Flux, the first conversational speech recognition model built for real-time voice agents, with sub-300ms end-to-end latency
• Keep voice inference inside your existing IAM, VPC, KMS, and CloudWatch posture, with no cross-region or cross-cloud transit
• Map your deployment to compliance scope across SOC, HIPAA, PCI DSS, FedRAMP, ISO, and GDPR
• Choose the right reference pattern for customer-facing voice agents, compliance recording and analytics, and augmented agent assistance across FSI and HCLS workloads

Built by the team that powers voice AI infrastructure at scale, trusted by 200,000+ developers.