firehose> #llmops

Total Cost of Inference

Eliminating the token bill does not eliminate the cost; it relocates it. This is the source's own correction to his own title — delivered, notably, right after his local model works: "there's really no such thing as free. You've got to find the balance between quality and price." Run a good model locally and you buy the hardware. Run it in someone's cloud and you buy a subscription or a VPS. Run a genuinely free hosted model and you pay in rate limits, in not knowing which model answered, or in capability. The bill is conserved; only its denomination changes.

The denominations the source demonstrates, in the order he pays them:

The operational conclusion follows, and it is not the video's title. Having demonstrated free twice, the source recommends paid-but-cheap: a small open model on a metered endpoint, which he prices at 14¢/40¢ per million tokens against Opus 4.6's $5/$25, "for like 50 to 100x cheaper rather than just being completely free — and I think that's still a huge win." Free is the demo; cheap is the deployment. The reason is that paid-but-cheap buys back the two denominations that actually hurt an agent: predictable routing and throughput.

There is a floor beneath which cheapness costs more than it saves. That is the correction this page makes to Model-Tier Routing's "route each sub-role to the cheapest tier that clears the bar" — the bar has to be priced in operator attention (a scarce, non-fungible currency), not just in dollars per token. A tier that saves $0.01 and costs four minutes of unsupervised spinning has not cleared it.

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