AWS SageMaker AI Uses Two Measurements to Set TTS Auto‑Scaling Targets

AWS deployed the 82‑million‑parameter Kokoro‑82M text‑to‑speech model on a SageMaker AI real‑time endpoint and replaced guesswork with two live‑endpoint measurements. The maximum concurrency a single instance can handle before breaching the service‑level objective (Q_max) was measured at 50 requests, and the end‑to‑end time for a scale‑out (T_total) was 320.2 seconds. These values were used to calculate precise scale‑out and scale‑in thresholds and cooldown periods, eliminating the hidden risk of over‑ or under‑provisioning that traditional auto‑scaling metrics miss.