Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

AWS Machine LearningOctober 22, 2026en

Teams deploying custom text-to-speech models on Amazon SageMaker AI often guess at autoscaling thresholds, and a wrong guess either wastes GPU spend or breaches a latency SLO. This post deploys Kokoro-82M as a custom container and derives a measured autoscaling policy from two live-endpoint measurements, Q_max and T_total, instead of guesswork.

This is a short summary published by AI Global Wire. The full article is owned and hosted by AWS Machine Learning — open it there to read it in full.

• Röst-AI
• Reglering