It depends on how you run it: locally the compute is effectively $0 (just electricity, after you own the hardware), a hosted API charges per token, and a rented GPU charges per hour — the cheapest option changes with your usage.
"How much does it cost to run an AI model?" has no single number, and any site that gives you one is hiding the real answer. The honest response is that there are three fundamentally different ways to run a model, each with a different cost structure, and the cheapest one depends entirely on your situation — your hardware, your usage volume, and your privacy needs. Making exactly this comparison transparent is the reason Spanvero exists.
Running locally, on hardware you already own, costs effectively $0 in compute. Your only ongoing expense is electricity, which for a single user is small. Whatever up-front cost your computer or GPU represents is already spent, and after that you can run the model as much as you like at no marginal cost — high personal volume is free. The catch is capability: the model has to fit in your VRAM (or spill into slower system RAM), so large models may run slowly or not at all. Local is the clear winner for personal use, development, privacy-sensitive work, and any model small enough for your hardware.
Using a hosted API means a provider runs the model and charges you per token — priced per million tokens, almost always with a cheaper rate for input (your prompt) and a pricier rate for output (the reply), because generating text costs more than reading it. You need no hardware and can reach very large models you couldn't run yourself, and it scales instantly. But cost scales directly with usage: light and bursty usage is cheap, heavy sustained usage gets expensive, and your data leaves your machine. The fair way to use an API is with your own key, paying the provider's real rate with no reseller markup. An API is usually cheapest and simplest for low, spiky, or occasional use, or for models too big to self-host.
Renting a cloud GPU means paying by the hour for a GPU and serving the model yourself, typically with a high-throughput engine like vLLM. You get big-model performance without buying hardware, and at sustained high volume it's frequently the cheapest option of all, because a flat hourly rate spread across a lot of tokens beats per-token pricing. The catch is that you pay whether the GPU is busy or idle, so it only wins when you keep it well-utilized, and you own the setup. Renting shines for steady, high-throughput workloads.
The factors that move the number, within any of these, are consistent: the model's size (bigger costs more to run and needs more memory), the quantization level (lower precision runs lighter and cheaper), how many tokens you push through (the workload), and your context length (longer contexts use more memory and, on APIs, cost more). So "cost to run" is really cost-per-token or cost-per-hour multiplied by how much you use — which is why your own volume is the deciding variable.
Here's the practical way to think about it. For occasional or light use, a pay-per-token API with your own key is usually cheapest and simplest — you pay pennies for what you actually use. For heavy personal use on a model that fits your hardware, local is unbeatable at $0 marginal cost. For sustained high-volume production traffic, a rented GPU running vLLM often wins once you're keeping it busy. The break-even between the API and rented-GPU routes is a real number set by your token volume and GPU utilization, and many projects start on an API and move to a rented GPU only as volume grows.
Spanvero computes all three costs for any open model with zero markup — the local $0-compute route (given the VRAM you'd need), the rent-your-own-GPU route at the vendor's real hourly price, and the bring-your-own-key API route at the provider's real per-token rate — so you can see the genuine cheapest option for your workload rather than guessing. Enter your own token volume at /calculator/, see live per-token prices at /trends/, and read the deeper breakdown of the three routes at /learn/local-vs-api-vs-renting/.
Local vs API vs renting a GPU · Tokens · What's the cheapest way to run a 70B model? · Is running AI locally cheaper than ChatGPT? · Is renting an H100 worth it? · Do I need a GPU to run local AI? · Inference · Quantization
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