Your bill tripled and nobody knows why. We find the leak.

“The bill went up 5x and the user count didn't move.”

That ratio rules out normal growth as the explanation. Something specific is firing far more often than it should, usually traceable to one endpoint or one background job.

“An AI API gets called on every page load, not just when something changed.”

A common pattern in prompt-generated code: a call to OpenAI or Claude wired directly into a render path with no caching, so ten thousand page views means ten thousand API calls for content that was identical on view one and view ten thousand.

“A background job retries forever instead of giving up and alerting someone.”

Without a retry limit, a job that fails for a structural reason (a bad input, a dead endpoint) just keeps consuming compute and API calls indefinitely instead of failing loudly once.

“Database reads are way higher than the number of pages being viewed.”

Usually an N+1 query — one query to get a list, then one more query per item in that list, instead of a single query with a join. Costs scale with data size, not with actual usage.

“Nobody set a spending cap or an alert on any provider.”

Most AI, database, and hosting providers support hard spending limits or usage alerts. Without one configured anywhere, the first sign of a leak is the invoice, not a warning.

“The spike started right after a specific feature or AI-assisted change shipped.”

The most useful data point you probably already have. We check git history and deploy timestamps against the billing timeline first, since the correlation usually points straight at the cause.