How Gemini API devoured $180 in a month: the real economy of AI

I analyzed a real combat case of using Google Cloud / Gemini API in an automated pipeline for creating white pages.

The task was not just to "use AI," but to understand the actual cost of one completed order.

In March, 79 white pages were created based on the model:

1 domain = $5
Total income: $395

After analyzing the Google Cloud billing, it turned out that the Gemini API used about:

$180.37 during the same period.

At first glance, almost half of the revenue went to AI. But after breaking down the expenses by SKU, it became clear where the problem was:

Text / logic / prompts / Cursor / tokens: about $29.62
Image generation: about $150.75

This means the text part was profitable — approximately 7.5% of the income.
The main budget drain occurred due to mass image generation through the API.

For one white page, about 39 images were generated.
At a price of approximately $0.039 per image, this amounted to about $1.52 just for images for one site.

Considering the text part, the AI cost for one site reached about:

$1.89 with a selling price of $5.

The main conclusion of the case:

AI itself does not make a project unprofitable.
Uncontrolled generation without limits becomes unprofitable.

After the analysis, practical rules were established:

— calculate AI expenses not "per month," but per order;
— separate text expenses from image generation;
— set limits on image generation;
— use budget alerts;
— do not keep unnecessary Google Cloud VMs if only the API is needed;
— use the free $300 trial credits from Google as a testing area, but calculate the economics as if it were real money.

As a result, it became clear: text Gemini / Cursor / API are profitable.
However, image generation through the API requires strict control.

Short conclusion:
AI is beneficial if managed.
Without limits, it eats into the margin.
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