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On October 23, 2026, AWS published a tutorial introducing a method for deriving autoscaling targets for TTS on Amazon SageMaker AI. The tutorial deploys the 82M-parameter TTS model Kokoro-82M as a custom container on a SageMaker AI real-time endpoint, and uses measured Q_max (the maximum concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to calculate the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The solution scales on the ConcurrentRequestsPerModel metric natively published by SageMaker AI, avoiding the hidden failure of custom metrics that no one reports. There are currently no further developments.
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No More Guesswork: A Measured Approach to TTS Autoscaling Targets on Amazon SageMaker AIDevelopments
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AWS publishes tutorial on deriving auto-scaling targets for TTS on SageMaker AI
Source:AWS Machine Learning
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Oct 23
AWS Machine LearningFirst-party
No More Guesswork: A Measured Approach to TTS Autoscaling Targets on Amazon SageMaker AI
AWS deploys the 82M-parameter TTS model Kokoro-82M in a custom container on an Amazon SageMaker AI real-time endpoint, using measured Q_max (the highest concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to derive the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The approach scales on the natively published SageMaker AI ConcurrentRequestsPerModel metric, avoiding the hidden failure of a custom metric that no one reports.