AWS demonstrates measured autoscaling for SageMaker AI TTS
- AWS deploys Kokoro-82M in a custom container on a SageMaker AI real-time endpoint and derives autoscaling settings from measurements on that endpoint.
- The workflow measures Q_max, the per-instance concurrency ceiling within the latency objective, and T_total, the time until added capacity serves healthy traffic.
- In the example, Q_max is 50 and T_total is about 320 seconds; the tooling uses these results to calculate scaling thresholds and cooldowns.
- The policy uses SageMaker AI’s ConcurrentRequestsPerModel metric, and deployment checks prevent scaling resources from being created without a measured plan.
Our take
Compared with relying on a managed inference toolkit, a custom container gives operators control over admission limits and concurrency signals, but adds deployment and container maintenance work. Instance provisioning and image pulls accounted for most of the measured scale-out delay, making smaller images or warm capacity practical levers before code-level tuning. The sample also pins an older PyTorch version with a known advisory, so its dependencies need review before production use.
Sources
- Stop guessing auto scaling targets for TTS on Amazon SageMaker AI · AWS Machine Learning
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