AI Costs Too Much. The Reckoning Is Here.

Inlate 2024, Heidi cofounder Yu Liu was shocked to discover that he was spending up to $3 million every month on the latest models from OpenAI, Anthropic and Google to power his note-taking startup for doctors. “We were making good revenues, but if I [subtracted] our AI bills, we barely had any margins,” Liu says.

At first, Liu ignored the outrageous costs, expecting the AI labs to lower their prices as intelligence became cheaper to serve and more abundantly available. That didn’t happen. Instead, the bills kept piling up. By mid-2025, he’d had enough. He turned to another startup, San Mateo, California–based Fireworks, which helped Heidi
build its own, cheaper AI using the 280 open models hosted on Fireworks’ site. Unlike with pricey, off-the-shelf models from the big American labs, developers can adjust how these models perform by feeding them data, creating customized models that bring down costs.

By moving 95% of his startup’s workload to specialized, open-weight models, Liu says he slashed his monthly AI bill by almost 90%. That’s significant savings for his business, which brought in $4.1 million in April. There were other benefits, too: While a standard model took 25 seconds to spit out transcriptions during peak hours, Heidi’s own bespoke model could do it almost instantly.

AI sticker shock has become the topic du jour for corporate America. For the last couple years, companies gave employees one mandate: Use as much AI as possible. Incentives became perverse. Performance reviews rewarded workers who burned through expensive tokens—the standard unit of AI usage. On internal leaderboards, bonus-obsessed “tokenmaxxing” employees competed against each other for titles like “token legend” and “AI god.”

“Companies are starting to open their eyes to the very clear future in which intelligence becomes the number one line item in everyone’s operating expenses for knowledge work,” says OpenRouter CEO and cofoun­der Alex Atallah, 34.