--- title: Autoscaling endpoints for LLM inference | daily.dev description: Autoscaling LLM inference deployments requires different thinking than traditional web services. GPU utilization metrics can appear healthy while request... canonical: https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Autoscaling endpoints for LLM inference | daily.dev og:description: Autoscaling LLM inference deployments requires different thinking than traditional web services. GPU utilization metrics can appear healthy while request... og:url: https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc og:image: https://api.daily.dev/og/posts/dDy21ncoC.png og:image:alt: Autoscaling endpoints for LLM inference og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Autoscaling endpoints for LLM inference **[Together AI](https://daily.dev/sources/togetherai)** · 10 min read · 0 upvotes · 0 comments ## Summary Autoscaling LLM inference deployments requires different thinking than traditional web services. GPU utilization metrics can appear healthy while request queues back up, and cold starts take minutes rather than seconds. Together AI's Dedicated Model Inference platform exposes inference-native autoscaling metrics including in-flight requests, TTFT, GPU utilization, and token throughput. The post explains how to choose the right metric, tune scale-up and scale-down windows asymmetrically (eager up, patient down), and budget for cold starts. An experiment replaying identical load under three policies (inflight_requests, ttft, gpu_utilization) shows that only the concurrency-based signal correctly detected saturation — TTFT stayed low due to continuous batching, and GPU utilization stayed under threshold for bursty short requests. The recommended default is inflight_requests with a target of 8, with latency or utilization metrics added only after observing real traffic patterns. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The Inference Bottleneck: Architecting Kubernetes Autoscaling for Production LLMs](https://daily.dev/posts/the-inference-bottleneck-architecting-kubernetes-autoscaling-for-production-llms-3igbdeoqq) · Container Journal · 1 upvotes · 0 comments - [Metrics that Matter with Serverless Inference](https://daily.dev/posts/metrics-that-matter-with-serverless-inference-9nynkhj1z) · DigitalOcean Community · 0 upvotes · 0 comments - [LLM Inference Cost Optimization: Run AI Inference for Less](https://daily.dev/posts/llm-inference-cost-optimization-run-ai-inference-for-less-syqrz0vbv) · Cast AI · 0 upvotes · 0 comments - [Configuring Dedicated Model Inference](https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr) · Together AI · 0 upvotes · 0 comments - [Beyond GPU Utilization: Building an End to End Observability Stack for LLM Inference on Kubernetes](https://daily.dev/posts/beyond-gpu-utilization-building-an-end-to-end-observability-stack-for-llm-inference-on-kubernetes-tjcterg7r) · Medium · 0 upvotes · 0 comments --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Autoscaling endpoints for LLM inference","url":"https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc"},"datePublished":"2026-07-31T17:48:33.645Z","dateModified":"2026-07-31T19:33:42.075Z","description":"Autoscaling LLM inference deployments requires different thinking than traditional web services. 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