Local AI vs Cloud API Cost Calculator (2026): Is Running LLMs Locally Cheaper?
"Just buy a 4090 and run it locally, it's cheaper than paying for an API" — is that actually true for your usage? Put in how much you use it and this tool compares three options: buying a GPU and running locally, paying a cloud API per token, or renting a GPU by the hour. It also tells you how many months it takes a local GPU to pay for itself. Method is below; every default is editable.
Renting from the cloud is the no-upfront option — see current rates in our cloud GPU pricing comparison, or go straight to RunPod. If you decide local makes sense, the GPU buying guide and the VRAM calculator tell you which card you actually need.
How this calculator works
- Cloud API cost = tokens/day × 30 × (price per 1M ÷ 1,000,000). Token throughput is what you pay for, so heavy/agentic use scales linearly.
- Local (buy) cost = electricity + amortized hardware. Electricity = watts ÷ 1000 × active hours/day × 30 × $/kWh. Hardware is spread over the months you choose (default 24). We don't add idle power — most home rigs sleep when not in use.
- Rental cost = rate/hour × active hours/day × 30. No upfront cost, but you pay whether the GPU is busy or just loaded.
- Break-even = GPU price ÷ (monthly cloud cost − monthly local electricity). If local electricity already exceeds cloud (very light use), it never breaks even — the API is simply cheaper for you.
The honest takeaway most calculators hide: buying a GPU only wins if you actually use it a lot. For light/occasional use, a cloud API is cheaper and zero-maintenance. Local wins on heavy, sustained, or privacy-sensitive workloads — and on the things money can't price: your data never leaving the building, no rate limits, no per-token meter running.
FAQ
Q: Does this include the cost of my time / electricity for the rest of the PC?
A: No — it isolates the GPU. A full system draws more at the wall (CPU, RAM, PSU losses); add ~50-80W if you want whole-system numbers. Your time setting up and maintaining a local stack is real but unpriced here.
Q: Why is the API sometimes cheaper even though "tokens are basically free locally"?
A: Because the GPU isn't free — you paid $1,000-$2,500 for it. At low usage, that amortized hardware cost per token is far higher than an API call. The API only loses when your volume is high enough to dilute the hardware cost.
Q: What about quality? A local 8B model isn't Claude Opus.
A: Correct — this compares cost, not capability. If you need frontier quality, local and cloud aren't substitutes. Local shines when an open model (Llama, Qwen, DeepSeek) is good enough for the task. See our best local models by VRAM.
Estimates for planning only. Defaults reflect roughly current US figures (electricity per EIA, GPU prices mid-2026); your real costs vary by region, model, and usage. Adjust the inputs.