Stop guessing auto scaling targets for TTS on Amazon SageMaker AI
A traffic spike hits your self-hosted text-to-speech (TTS) endpoint, and requests start queueing.

Proof1 independent outlet
Key points
- The auto scaling policy behind that endpoint was supposed to prevent this, but the target value it scales on was a guess, and guesses like that fail silently until real traffic proves them wrong.
- In this post, we deploy Kokoro-82M, an 82-million-parameter TTS model, as a custom container on an Amazon SageMaker AI real-time endpoint.
- Why default auto scaling metrics don’t work for TTS
- Most public guidance on Amazon SageMaker AI auto scaling targets short synchronous request/response calls, or large language model (LLM) serving, where the natural metric is tokens generated per second.
Sources (1)
- [1]Stop guessing auto scaling targets for TTS on Amazon SageMaker AIAWS Machine Learning Blog · Oct 22, 04:30 PM
A traffic spike hits your self-hosted text-to-speech (TTS) endpoint, and requests start queueing.
The auto scaling policy behind that endpoint was supposed to prevent this, but the target value it scales on was a guess, and guesses like that fail silently until real traffic proves them wrong.
Extractive summary: sentences quoted from the sources.
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