It can be, but 'local' isn't free — you pay for hardware and electricity instead of a subscription or per-token fees; for light use a hosted service is often cheaper, while heavy use, privacy, and offline access favor local.
This is one of the most common questions from people considering the switch to open models, and the honest answer resists a simple yes. Running AI locally can be cheaper than paying for a hosted service like ChatGPT — but only in the right circumstances, and "local" is emphatically not $0 in the way people sometimes assume. Being clear-eyed about the real costs on both sides is the whole point.
Start by naming the true cost of local. When you run an open model on your own machine, the compute is effectively free — but you've already paid for the hardware, and you keep paying for electricity. If you buy a GPU specifically to run models, that up-front cost is real and has to be counted. If you're using a computer you already own, the marginal cost is just electricity, which for a single user is small. So local is "cheap" mainly when the hardware is already sunk cost or when your usage is heavy enough that the per-token savings pay back the hardware over time. It is not free in the sense of costing nothing at all.
Now the other side. A hosted subscription (a flat monthly fee for a consumer chat product) or a pay-per-token API charges you for the provider's hardware, operation, and the convenience of never managing any of it. For light or occasional use, this is often genuinely cheaper than buying hardware — you might pay a modest monthly fee or a few dollars of API usage and get access to very large, top-tier models you couldn't run at home. The metered cost only becomes painful at high, sustained volume, where per-token or per-seat fees add up and a one-time hardware purchase (or a well-utilized rented GPU) starts to win.
So the cost comparison hinges on your volume and what you already own. If you use AI lightly and don't already have capable hardware, a hosted service is usually cheaper — buying a GPU to save on light usage rarely pays off. If you use AI heavily, run it on hardware you already own, or would otherwise pay for many seats or a large token volume, local can be dramatically cheaper because the marginal cost per query drops to near zero. There's a break-even point set by how much you use it against the hardware you'd need.
But cost is only part of the real decision, and the honest case for local often rests on the other benefits. Privacy is a big one: with a local model, your data never leaves your machine, which matters for sensitive work in a way no hosted service can match. Offline access is another — a local model works with no internet at all. And there's no rate limiting, no per-token meter running, and no dependence on a provider's uptime, pricing changes, or model deprecations. For many people who choose local, these — not raw cost — are the deciding factors, with the cost being a wash or a modest win.
A fair caveat about capability: the largest hosted models are extremely capable, and the best open model you can run on your own hardware may not match the very top of the hosted tier, especially if your VRAM limits you to smaller models. For many everyday tasks a good local model is more than sufficient, and the privacy and cost benefits win — but if you specifically need frontier-level capability and only use it occasionally, paying for a hosted service or an API is the rational, often cheaper, choice. Match the tool to the task and the volume.
The genuinely fair way to compare, if you do go the API route, is to use your own API key and pay the provider's real per-token rate with no reseller markup — which is exactly how Spanvero frames the comparison.
Spanvero exists to make this decision honest and specific to you. For any open model, it shows the real cost of running locally ($0 compute given the VRAM you'd need), renting a GPU, and using your own API key — with zero markup — so you can compare against a subscription for your actual usage. Enter your volume at /calculator/, see live per-token prices at /trends/, and read the full three-way breakdown in the guide at /learn/local-vs-api-vs-renting/.
Local vs API vs renting a GPU · How much does it cost to run an AI model? · How do I run AI privately and offline? · Tokens · Do I need a GPU to run local AI? · Is renting an H100 worth it? · What GPU should I buy for running local LLMs? · How do I choose which AI model to run?
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