Cloud API vs Your GPU: AI Cost Calculator

How the calculation works

The cloud side is pure arithmetic. Every provider prices in dollars per million tokens, split into a cheaper input (prompt) rate and a pricier output (generation) rate. Your monthly cost is just:

(requests/day × input tokens ÷ 1M × input price) + (requests/day × output tokens ÷ 1M × output price), all × 30.4 days

At 500 requests a day with 2,000 input and 500 output tokens, you push 30.4M input and 7.6M output tokens a month. On Claude Sonnet 4.5 ($3/M in, $15/M out) that is roughly $91 + $114 = $205 every month, forever, and it grows linearly the moment your traffic does.

The cost of running your own GPU

Here is the part the comparison sites leave out: if you already own the GPU, the marginal cost of an extra token is electricity, not hardware. A card only draws full power while it is actively generating. We estimate generation throughput at 30 tokens/second for a 7B–14B class model — a realistic figure for an RTX 4070-class card running a Q4 GGUF — and compute how many GPU-hours per day your output volume needs:

GPU hours/day = (requests/day × output tokens) ÷ 30 tok/s ÷ 3600

Multiply by the card's sustained wattage and your electricity rate and you get the daily energy cost. A 4090 pulling 450W that is busy 70 minutes a day at $0.15/kWh costs about 8 cents. That is the whole bill.

But the GPU wasn't free

True — and that is the honest counter-argument. This tool deliberately treats the GPU as a sunk costbecause for most people it is: it is the card already in your gaming rig, workstation, or Mac. You are not buying hardware to save money; you are putting hardware you own to work instead of renting someone else's. If you would have to buy a card purely for inference, add its price divided by the months you expect to keep it (a $1,600 card over 3 years is ~$44/month) to the electricity figure before comparing.

Hidden costs on both sides

Cloud wins on near-zero usage and on giant frontier models you cannot run locally. Local wins on steady, high-volume, or privacy-sensitive workloads — coding agents, batch jobs, RAG pipelines that re-read the same documents thousands of times.

When does self-hosting pay off?

The break-even point is wherever the amber savings number above crosses zero. For light, bursty chat use, the API is genuinely cheaper and simpler. But the 5× and 10× rows in the table show how fast the cloud bill compounds. A single coding agent running 32k-context requests all day can burn through tens of millions of tokens a week — exactly the workload where a GPU you already own quietly wins.

Where Wide Area Intelligence fits

Wide Area Intelligence is the bridge: it exposes the GPU in your own machine as an OpenAI-compatible endpoint at https://wideareaai.com/api/v1, with automatic cloud failover for the occasional request that needs a frontier model. You get the electricity-only economics for the bulk of your traffic and the cloud as a safety net — without choosing one forever. Add a node from the dashboard in one command, no port forwarding.