Originally published on AI Tech Connect.
When self-hosting on vLLM beats an API Every team that ships a language-model feature eventually faces the same fork in the road: keep paying a managed API by the token, or self-host an open-weight model on your own graphics processing units? There is no universal answer, but there is a clear decision rule. Self-hosting on vLLM wins when three things are true at once. First, your traffic is high and reasonably steady, so a GPU stays busy rather than idling. Second, you need control the API will not give you — a specific open-weight model, a custom fine-tune, tight data residency, or predictable latency you can tune. Third, your volume is large enough that a fixed GPU-hour bill undercuts a per-token bill. When those three line up, owning the serving stack is genuinely cheaper and gives you…
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