What building an application with AI really costs: $2,000, zero users

An interface designer reports spending $2,000 on vibe coding tools for almost no users, with small features costing over $10 apiece. The expense comes from everything that had to be rewritten after the first code was produced.

What building an application with AI really costs: an interface designer reports on Reddit (r/vibecoding, May 2026) spending $2,000 on Codex, Claude Code, and OpenCode for almost no users, noting that a small feature sometimes cost more than $10 in usage.

The advertised price and the real one

The author has worked in interface design for almost five years and is no beginner. They say Claude Code understood their intentions best, while cost remained the problem. The unit price hurts more than the total: over ten dollars for a small feature, in their words. At that rate, a thirty-feature application costs three hundred dollars if everything works first time, and nobody builds a product first time. A total of that size is usually explained by what follows the first draft, when the same feature goes through checkout again.

Expensive generosity

Another user describes the mechanism in a different tool (r/lovable, June 2026): AI loves helping, so you request a change and it 'improves' four other things you never mentioned, half of which break, and you then spend credits undoing its generosity. Their countermeasure is one instruction, 'do not touch anything else', and they estimate roughly halving consumption. That estimate is theirs, without a published usage record, but the mechanism is among the documented pitfalls of AI development encountered in our own measurement campaigns.

What drives the bill up, line by line

Four expenses hide behind a two-thousand-dollar total. Rereading the project at every exchange, as the assistant reloads context to understand where it is working, a reread growing with the product. Failed attempts, charged like every other attempt, without a screen distinguishing them. Collateral damage, fixed through new exchanges that sometimes create more. And the quietest one: a feature that already existed, rebuilt because nobody explicitly wrote that it existed or what it should do. A scope document eliminates the fourth and reduces the first three without removing them.

When trial and error still makes financial sense

This reasoning has a limit worth stating. For an idea you want to test this weekend, ten dollars of experimentation costs less than an evening writing a specification, and far less than an agency quote. Conversational tools win on day one, without debate: they display a result while the method still asks you to read documents. The calculation reverses when the product must last, generate revenue, and change hands. One question decides it, asked on day one: should what you are building still exist in two years? While the answer remains uncertain, experimentation is the right purchase.

The three safeguards that change the calculation

The first is written scope: a specification approved before construction states what exists and what remains, so the same feature is not paid for twice. The second is a test written before code: a step is declared finished when it passes verification; otherwise a targeted repair is triggered and counted. The third is a cap set before spending: a bounded estimate, monthly budget, queue, and maximum repair count before the decision returns to you. We publish our own amounts campaign by campaign in our bills, line by line.

Estimate your case tonight

Take the last change you requested from an AI tool and note three numbers: the displayed exchange cost, the number of back-and-forth rounds required, and the number of things broken along the way. Multiply by your product's feature count. If the result exceeds what you were willing to spend, the missing document contributes more than token prices, and what rises while token prices fall explains the rest of the bill. Repeat after your first scope document: the difference between those records is worth more than a pricing promise.