---
type: "post"
id: "stop-guessing-auto-scaling-targets-for-tts-on-48e817"
slug: "stop-guessing-auto-scaling-targets-for-tts-on-48e817"
title: "Stop guessing auto scaling targets for TTS on Amazon SageMaker AI"
description: "AWS has published a technical guide for deriving autoscaling targets for a text-to-speech endpoint on Amazon SageMaker AI."
retrieval_nugget: "AWS has published a technical guide for deriving autoscaling targets for a text-to-speech endpoint on Amazon SageMaker AI."
published_at: "2026-10-22"
updated_at: "2026-10-22"
record_date: "2026-10-22"
date_kind: "published_at"
topics: ["inference","ai-infrastructure","generative-media"]
entities: ["Amazon Web Services","Amazon SageMaker AI","Kokoro-82M","AWS Cloud Development Kit","Amazon CloudWatch"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "Adds a TTS-specific guide for choosing autoscaling targets, distinct from the prior guide on endpoint concurrency sweeps."
prior_refs: ["memory:f07152ce-6749-893e-af93-94b4286b59a3"]
source_url: "https://aws.amazon.com/blogs/machine-learning/stop-guessing-auto-scaling-targets-for-tts-on-amazon-sagemaker-ai"
source_urls: ["https://aws.amazon.com/blogs/machine-learning/stop-guessing-auto-scaling-targets-for-tts-on-amazon-sagemaker-ai"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:stop-guessing-auto-scaling-targets-for-tts-on-48e817"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
routes: {"html":"https://newruntime.com/posts/stop-guessing-auto-scaling-targets-for-tts-on-48e817/","markdown":"https://newruntime.com/posts/stop-guessing-auto-scaling-targets-for-tts-on-48e817.md","json":"https://newruntime.com/posts/stop-guessing-auto-scaling-targets-for-tts-on-48e817.json"}
---

# Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

## Retrieval answer

AWS has published a technical guide for deriving autoscaling targets for a text-to-speech endpoint on Amazon SageMaker AI.

AWS has published a technical guide for deriving autoscaling targets for a text-to-speech endpoint on Amazon SageMaker AI. The example deploys the 82-million-parameter Kokoro-82M model in a custom container on a SageMaker AI real-time endpoint, with deployment handled through AWS CDK stacks.

The guide bases its policy on two measurements from a live endpoint: `Q_max`, the highest concurrency a single instance can sustain without breaching a latency SLO, and `T_total`, the end-to-end scale-out time. AWS says these values can be used to calculate scale-out and scale-in thresholds and cooldowns. The endpoint scales on SageMaker AI's native `ConcurrentRequestsPerModel` metric.

AWS notes that the metric reports a Maximum over a 10-second window, while the load client measures mean in-flight requests. Its tooling measures the relationship between the two at each concurrency level; in the reported Kokoro-82M run, the ratio was 1.0. The source does not establish that these thresholds, ratios, costs, or latency results will transfer to other models, traffic patterns, instance types, or SLOs.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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