Self-hosting vs API: Calcolatore del punto di pareggio dei costi per LLM

Convieni acquistare una GPU e auto-ospitare un LLM open source, oppure continuare a pagare per token utilizzati tramite un’API? Dipende dal volume. Inserisci il tuo utilizzo mensile e la tua configurazione hardware: questo calcolatore ti mostra il punto di pareggio, ovvero il momento in cui possedere la GPU diventa più economico rispetto alla bolletta dell’API.

Ricorda: l'auto-hosting esegue modelli open-weight, quindi considera la differenza di qualità rispetto a un'API di ultima generazione — e utilizza il nostro Calcolatore VRAM per verificare che la tua GPU possa effettivamente eseguire il modello desiderato.

Quick answer: Is it cheaper to self-host an LLM or use an API?

It depends almost entirely on your sustained monthly token volume. For most users a hosted API is cheaper: below roughly 50 million tokens a month, per-token pricing beats the cost of buying and running a GPU. Self-hosting only pays off at high, steady volume — typically tens to hundreds of millions of tokens a month feeding agents, batch jobs or a whole team — where an owned GPU kept busy amortises its upfront cost against effectively unlimited inference. The break-even is a range, not a fixed line: it moves with model size, GPU tier, electricity price and, above all, how heavily you keep the card utilised.

• Low volume (under ~50M tokens/month): the API wins almost every time.
• The decision zone (~50M–500M tokens/month): a genuine toss-up, but only worth self-hosting if you have dedicated engineering time to run it.
• High sustained volume (agents, batch or a whole team, 500M+ tokens/month): a well-utilised owned GPU can cut inference cost by up to ~5x.
• A GPU only pays for itself if it is kept busy — an idle card still costs its full capex and power.
• Budget “flash” or small-model APIs are so cheap that self-hosting is rarely justified at any volume.

Domande frequenti

When does a local GPU pay for itself?

A GPU pays for itself once your steady API spend on an equivalent open model consistently exceeds the all-in cost of owning one. A consumer GPU of roughly $2,000–$2,500 spread over three years works out to about $55–$70 a month, plus $30–$60 in power — so owning one costs roughly $85–$130 a month all-in. If your comparable API spend stays under that, the API is cheaper; once it runs well above it — on the order of a few hundred dollars a month — the card recovers its $2,000–$2,500 upfront cost within about a year and saves money after that.

How much VRAM do I need to run an LLM locally?

At 4-bit quantisation, budget roughly 0.5–0.6 GB of VRAM per billion parameters, plus extra for the KV cache that grows with context length. In practice a 7B model needs about 8 GB, a 13B model 12–16 GB, and a 4-bit 70B model roughly 40–48 GB. That means a 7–13B model fits on a single 8–16 GB card, but a 4-bit 70B is too big for any single consumer GPU — even a 32 GB card falls short, so it takes two 24 GB cards, a 48 GB workstation card, or a smaller, more aggressively quantised model.

What are the hidden costs of self-hosting an LLM?

The GPU price is only part of it — once you add electricity, cooling and especially engineering time, self-hosting typically costs 3–5x the raw GPU rental figure. Plan for 10–20 hours a month of setup, monitoring and maintenance; that labour, plus the cost of a card sitting idle between jobs, is what sinks most naive break-even estimates.

Does the model size change the break-even point?

Yes. Bigger models need more or pricier GPUs, which raises the capex you have to amortise, but they also carry the highest per-token API prices, which pushes the break-even lower in volume terms. A small 7–13B model is cheap to self-host but also cheap via API, so APIs usually win; a 70B-plus open model is where a busy owned GPU tends to deliver the largest savings.

Does electricity make self-hosting too expensive?

Usually not — power is a minor line item next to the hardware. A single desktop GPU run flat out draws roughly 400–600 W, which at typical electricity rates works out to only around $30–$60 a month if it runs continuously. The dominant cost of self-hosting is the upfront hardware and the engineering time to operate it, not the electricity.

Should a small team or startup self-host or use an API?

Start with an API. Until you have high, predictable volume and someone to run the infrastructure, per-token pricing is cheaper, faster to ship and carries no upfront risk. Self-hosting is worth revisiting only when your monthly bill is large and stable — typically once you are consistently past tens of millions of tokens a month on a comparable open model.