2 posts tagged with "cost"

About a week back, I was talking with a customer, and they started sharing about their AI initiatives. During the conversation, they mentioned that their token budget for the year was already gone. It lasted only four months. They did estimates and pilot runs, and still could not get it right.

And to my surprise, I found they are not alone. In April, Uber's CTO said the company had already used up its planned 2026 AI budget, only months into the year, after a surge in the use of AI coding tools.

Different industries, use cases, and even usage patterns. But the same problem: token consumption was way more than anyone had budgeted for.

A 2026 survey of 100 enterprise CIOs found 37% now run five or more AI models in production, up from 29% the year before. A separate Zapier survey of enterprise leaders, published the same year, found 81% at least somewhat worried about depending too heavily on one AI vendor, and 47% said losing their primary vendor would disrupt a real business function. That fear is exactly what "model platforms" like Amazon Bedrock, Azure OpenAI Service, and Google's Vertex AI Model Garden are sold as the fix for: one integration, many models, switch providers without rewriting your app.

It's a good pitch. It's also not quite true. Routing your calls through a hyperscaler's managed layer doesn't remove a dependency, it relocates one. You stop depending on a single model provider and start depending on a single cloud provider instead, and that second dependency is usually stickier than the first.