---
title: "Comment by OneLifeVibe on \"The real cost of AI inference is not the token price\" | Hashnode"
description: "This reframing really landed for me — especially the point that the cost that matters is cost per correct answer, not cost per million tokens. The retry example"
url: "https://hashnode.com/posts/the-real-cost-of-ai-inference-is-not-the-token-price/6aefd91aa29828b0eb105088/comment/6af0dc26d009d0bbed9f5d51"
---

[Discussion](https://hashnode.com/posts/the-real-cost-of-ai-inference-is-not-the-token-price/6aefd91aa29828b0eb105088)

Comment [The real cost of AI inference is not the token price](https://hashnode.com/posts/the-real-cost-of-ai-inference-is-not-the-token-price/6aefd91aa29828b0eb105088)

[OneLifeVibe](https://hashnode.com/@onelifevibe)

[4d ago](https://hashnode.com/posts/the-real-cost-of-ai-inference-is-not-the-token-price/6aefd91aa29828b0eb105088/comment/6af0dc26d009d0bbed9f5d51)

This reframing really landed for me — especially the point that the cost that matters is cost per correct answer, not cost per million tokens. The retry example stuck with me: a cheap model that needs three attempts can quietly end up more expensive than a pricier one that gets it right first time. I also liked the practical order you suggest, fixing prompts and context first before reaching for a bigger model, since that is where a lot of wasted calls seem to come from. Do you find teams usually discover this from a bill shock, or from actually instrumenting latency and retry rates?