---
title: "Understanding the Cost of Generative AI Models in Production"
url: https://daily.dev/posts/understanding-the-cost-of-generative-ai-models-in-production-hpjy5064n
source_url: https://www.philschmid.de/cost-generative-ai
type: article
source: "Philipp Schmid"
published: 2026-05-31T07:41:20.639Z
updated: 2026-05-31T08:25:52.253Z
tags: ["kubernetes", "llm", "genai", "mlops"]
reading_time: 4
upvotes: 0
comments: 0
language: en
---

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# Understanding the Cost of Generative AI Models in Production

**[Philipp Schmid](https://daily.dev/sources/philschmid)** · 4 min read · 0 upvotes · 0 comments

## Summary

Deploying generative AI models in production involves far more than raw GPU compute costs. A comprehensive total cost of ownership (TCO) analysis must account for container orchestration (Kubernetes), networking, load balancing, autoscaling, logging, and CI systems. Using a hypothetical A100 80GB deployment, the post shows that infrastructure costs alone add ~27% over raw compute. When factoring in even one ML engineer's salary for maintenance, a self-built solution becomes significantly more expensive than a managed service — requiring that engineer to manage ~43 models just to break even. The post argues that managed services often deliver better long-term value, and warns against being misled by aggressively priced compute from VC-subsidized startups.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.philschmid.de/cost-generative-ai>

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---

Tags: [#kubernetes](https://daily.dev/tags/kubernetes), [#llm](https://daily.dev/tags/llm), [#genai](https://daily.dev/tags/genai), [#mlops](https://daily.dev/tags/mlops)

[View this post on daily.dev](https://daily.dev/posts/understanding-the-cost-of-generative-ai-models-in-production-hpjy5064n)
