--- title: Stop Wasting GPU Budget: Autoscaling AI Inference on... description: Standard Kubernetes HPA fails for AI inference workloads because it relies on CPU/memory metrics while GPU compute and VRAM are the real bottlenecks. KEDA... canonical: https://daily.dev/posts/stop-wasting-gpu-budget-autoscaling-ai-inference-on-kubernetes-with-keda-xr0hnhign twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA | daily.dev og:description: Standard Kubernetes HPA fails for AI inference workloads because it relies on CPU/memory metrics while GPU compute and VRAM are the real bottlenecks. KEDA... og:url: https://daily.dev/posts/stop-wasting-gpu-budget-autoscaling-ai-inference-on-kubernetes-with-keda-xr0hnhign og:image: https://api.daily.dev/og/posts/xR0HNHIgN.png og:image:alt: Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA 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. # Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA **[Cloud Native Now](https://daily.dev/sources/cloudnativenow)** · 3 min read · 0 upvotes · 0 comments ## Summary Standard Kubernetes HPA fails for AI inference workloads because it relies on CPU/memory metrics while GPU compute and VRAM are the real bottlenecks. KEDA (Kubernetes Event-driven Autoscaling) solves this through a three-layer architecture: a telemetry layer using NVIDIA DCGM, a custom external scaler (keda-gpu-scaler, open-sourced on GitHub) that translates GPU metrics into KEDA-compatible signals, and an execution layer that scales inference pods up or down — including scale-to-zero. Scale-to-zero is highlighted as the key cost-saving feature, allowing idle GPU workloads to be fully terminated during off-hours rather than burning expensive compute 24/7. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Kubernetes GPU Autoscaling: Scale GPU Capacity to Real Demand](https://daily.dev/posts/kubernetes-gpu-autoscaling-scale-gpu-capacity-to-real-demand-qaljjegbw) · Cast AI · 2 upvotes · 0 comments - [KEDA: How Event-Driven Autoscaling Cuts Kubernetes Cost](https://daily.dev/posts/keda-how-event-driven-autoscaling-cuts-kubernetes-cost-dqoxqci3j) · Cast AI · 0 upvotes · 0 comments - [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 --- Tags: [#kubernetes](https://daily.dev/tags/kubernetes), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/stop-wasting-gpu-budget-autoscaling-ai-inference-on-kubernetes-with-keda-xr0hnhign) ```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":"Stop Wasting GPU Budget: Autoscaling AI Inference on Kubernetes with KEDA","url":"https://daily.dev/posts/stop-wasting-gpu-budget-autoscaling-ai-inference-on-kubernetes-with-keda-xr0hnhign","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/stop-wasting-gpu-budget-autoscaling-ai-inference-on-kubernetes-with-keda-xr0hnhign"},"datePublished":"2026-06-08T20:58:55.918Z","dateModified":"2026-06-08T21:03:21.690Z","description":"Standard Kubernetes HPA fails for AI inference workloads because it relies on CPU/memory metrics while GPU compute and VRAM are the real bottlenecks. 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