Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

Amazon Web Services · Joseph Cottingham · 22 October 2026, 16:30

AWS SageMaker AI users can get TTS auto scaling wrong because policies may be based on a guessed concurrency target that fails silently until a traffic spike queues requests and breaks the latency SLO for listeners.

Why it matters

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.