Originally published on lavkesh.com

Our AI feature was humming, then the cloud bill tripled overnight, and nobody had warned us about that part.

For three years the industry chased bigger training runs, bragging about model size, data volume, and benchmark scores. Training makes headlines, but it’s a one‑off expense.

In early 2026 inference spend finally overtook training spend. Fifty‑five percent of AI cloud infrastructure now powers inference, up from about thirty percent three years ago, and analysts expect seventy to eighty percent by year‑end.

During development the API calls are a few hundred a day, the cost looks like noise, and everything feels fine. Deploy to real users and those calls explode from hundreds to millions, and the per‑token rate that looked reasonable on a spreadsheet becomes a massive line item.

Gartner reports cost estimation errors of five hundred to one thousand percent for companies scaling AI. A budget of two hundred thousand dollars can swell to two million once production traffic hits.

Agentic workflows amplify the problem. The inference meter ticks not only when a user speaks, but also when the agent decides, calls a tool, re‑reads context, or loops. One interaction can fire twenty model calls that never appear in the code.

The remedy is simple. Treat inference like any other engineering concern. Route simple tasks such as classification, extraction or short answers to smaller, cheaper models, and reserve the big model for the hard cases.

You also need visibility. Track cost per user, per feature, and identify which endpoint is responsible for most of the spend. Most teams lack that data because they never built the observability into the pipeline.

The conversation is shifting from which model wins to how efficiently you run it. IBM said this year that models are becoming commodities, and the differentiator is now the infrastructure that serves them. Expect inference to generate more on‑call pages than training ever did.