--- title: Deployment Isn't the Bottleneck: Inference Economics Is description: Deploying an AI model is easy; keeping it affordable is the real challenge. The piece breaks down three interconnected cost drivers: cold starts (the minutes... canonical: https://daily.dev/posts/deployment-isn-t-the-bottleneck-inference-economics-is-joy2kikzs twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Deployment Isn't the Bottleneck: Inference Economics Is | daily.dev og:description: Deploying an AI model is easy; keeping it affordable is the real challenge. The piece breaks down three interconnected cost drivers: cold starts (the minutes... og:url: https://daily.dev/posts/deployment-isn-t-the-bottleneck-inference-economics-is-joy2kikzs og:image: https://api.daily.dev/og/posts/jOy2kiKzS.png og:image:alt: Deployment Isn't the Bottleneck: Inference Economics Is 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. # Deployment Isn't the Bottleneck: Inference Economics Is **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 11 min read · 0 upvotes · 0 comments ## Summary Deploying an AI model is easy; keeping it affordable is the real challenge. The piece breaks down three interconnected cost drivers: cold starts (the minutes it takes an idle GPU to load a model and produce a first token), GPU utilization (batching requests so the GPU's compute isn't wasted during the memory-bound decode phase), and cost per inference (GPU hourly rate divided by requests served per hour). It walks through worked numbers showing a thirteen-fold cost difference between well-utilized and poorly-utilized identical setups, explains why continuous batching frameworks like vLLM, TensorRT-LLM, and SGLang outperform plain PyTorch loops, and argues that deployment choices (model size, quantization, serving framework, number of warm replicas) should follow from the economics rather than precede them. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does cost per token vary so much between teams running the exact same LLM on the same GPU hardware? The difference comes down to batch size, not hardware or model differences. During the decode phase of LLM inference, the GPU reads the entire set of model weights from memory to produce each token, so processing one request at a time wastes most of the GPU's compute capacity. Running a batch size of 64 instead of 1 produces far more tokens per memory read, directly lowering cost per token even on identical hardware. _When comparing inference setups for a project, daily.dev surfaces practical breakdowns of GPU batching and cost tradeoffs._ ### How long does a cold start take for a self-hosted LLM and what does it cost to avoid it? A cold start for a mid-sized language model commonly takes two to five minutes, and can be longer for a 70B model, which is far too long for a user waiting on a chat response. Avoiding this by keeping one GPU replica always warm costs roughly $2,200 per month at $3 per hour, before serving a single request, turning the cold start problem into a cost tradeoff rather than a technical fix. _Teams weighing warm-replica costs against cold start latency can track these tradeoffs through daily.dev._ ### What is the formula for calculating cost per inference request when self-hosting an LLM? Cost per request equals the GPU's hourly rental rate divided by the number of requests served per hour. The denominator hides most of the complexity, since requests per hour depends on utilization, batch size, average prompt and output length, and how much of the hour the GPU is actually warm and receiving traffic; a poorly batched, low-traffic setup can cost thirteen times more per request than a well-utilized identical setup. _Engineers sizing self-hosted inference costs against hosted API pricing can follow this kind of cost modeling on daily.dev._ ## Similar posts on daily.dev - [Most Teams Move to Dedicated Inference Too Early](https://daily.dev/posts/most-teams-move-to-dedicated-inference-too-early-onm3p4e4n) · DigitalOcean Community · 1 upvotes · 1 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 - [Unpacking the deceptively simple science of tokenomics](https://daily.dev/posts/unpacking-the-deceptively-simple-science-of-tokenomics-egfxwxsqv) · The Register · 2 upvotes · 0 comments - [Serverless vs. On-prem vs. Edge Deployment](https://daily.dev/posts/serverless-vs-on-prem-vs-edge-deployment-kebs8beeq) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [The New Economics of AI: Balancing Training Costs and Inference Spend](https://daily.dev/posts/the-new-economics-of-ai-balancing-training-costs-and-inference-spend-enbomvtpr) · finout · 0 upvotes · 0 comments --- Tags: [#finops](https://daily.dev/tags/finops), [#ai-inference](https://daily.dev/tags/ai-inference), [#mlops](https://daily.dev/tags/mlops), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/deployment-isn-t-the-bottleneck-inference-economics-is-joy2kikzs) ```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":"Deployment Isn't the Bottleneck: Inference Economics Is","url":"https://daily.dev/posts/deployment-isn-t-the-bottleneck-inference-economics-is-joy2kikzs","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/deployment-isn-t-the-bottleneck-inference-economics-is-joy2kikzs"},"datePublished":"2026-10-09T19:17:01.941Z","dateModified":"2026-10-09T20:08:02.665Z","description":"Deploying an AI model is easy; keeping it affordable is the real challenge. 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During the decode phase of LLM inference, the GPU reads the entire set of model weights from memory to produce each token, so processing one request at a time wastes most of the GPU's compute capacity. Running a batch size of 64 instead of 1 produces far more tokens per memory read, directly lowering cost per token even on identical hardware. When comparing inference setups for a project, daily.dev surfaces practical breakdowns of GPU batching and cost tradeoffs."}},{"@type":"Question","name":"How long does a cold start take for a self-hosted LLM and what does it cost to avoid it?","acceptedAnswer":{"@type":"Answer","text":"A cold start for a mid-sized language model commonly takes two to five minutes, and can be longer for a 70B model, which is far too long for a user waiting on a chat response. 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