Two facts shaped this company.
The first is that the cost of running a forward pass on a Blackwell-class GPU is not what your hyperscaler invoice says it is. The hardware is dramatically more capable than it was three years ago. The pricing has not moved with the silicon. Margin has filled the gap.
The second is that the inference engines most teams run in production were written for an academic publication, not for a customer's SLA. The throughput numbers in those papers are real. They are also leaving an order of magnitude on the floor when you put them into a managed endpoint behind a load balancer.
We started iframe.ai to do something about both. The first hire was a researcher; so was the third, the seventh, and the fifteenth. The cluster came before the marketing site. The runtime shipped before the pricing page had a second column. We wrote the papers before we wrote the headlines.
Today the company sells two things: production GPU capacity at one-third the price of the dominant clouds, and a managed inference platform that runs the open-source model catalog at twenty times the throughput of the reference implementations. Both of those numbers come from research output. Both ship as products you can put on a credit card or sign a three-year contract for.
We are not the largest cloud. We are not the loudest. We are the most affordable production-grade GPU infrastructure on the market — and we are willing to show our work, in writing, in eval data, in the CSV files behind every benchmark on this site. If that is the kind of partner you want for the next training run, the next inference platform, or the next quarter's capacity plan, we would like to hear from you.
— The founding team, iframe.ai