“We see a shift happening between the cheaper open source models and the bigger expensive models,” Vogels said in an interview on the sidelines of the UN’s AI for Good summit. 

Stories of runaway AI bills have been making some executives skittish about building systems on the most advanced models from companies such as OpenAI, Anthropic, and Google DeepMind, that bill by the token. (A token is the basic unit of data an AI model processes, equivalent to about a word and a half of English language text.) Uber said it burned through its entire 2026 AI budget in four months, while company reportedly burned through half a billion dollars in a single month after failing to cap AI usage for employees have caused concern across industries. 

Fears of runaway spending are forcing companies to rethink how—and where—they deploy the most powerful frontier models. While large models from companies like OpenAI, Anthropic, and Google often deliver top-tier performance, they also come with significantly higher operating costs, particularly when deployed at scale. Open source models (also sometimes referred to as “open weight” models) can usually be downloaded for free but then users have to pay for their own cloud computing infrastructure on which to run the models. Still, this often works out to be cheaper than using the most advanced proprietary models.

“Cost is a very important part of your architecture, you need to take that into account,” Vogels said. “Do you really need to have the biggest, highest‑end model to solve this? The answer is no, you don’t.”

The shift also reflects a broader maturation in how companies are thinking about AI adoption. After an initial wave of experimentation fueled by hype and rapid advances in large language models, many organizations are now entering a more pragmatic phase focused on return on investment. That means scrutinizing not just what AI can do, but what it costs to deploy and maintain over time.

While customers may be shifting toward open‑source models as a cheaper option, Vogels also said companies were also putting a premium on transparency and trust in how models are trained and deployed

“Transparency becomes extremely important,” he said. “People want to know what is the data that goes into it.”

That demand is particularly acute in sectors like healthcare, government, and humanitarian work, where understanding how an AI system was trained—and how it makes decisions—can be as important as its performance. “If these people serve vulnerable communities. If they don’t trust the system, they won’t use it,” Vogels said.

Open-source models, which allow developers to inspect and modify code and more easily fine tune the model on their own data, are often seen as better aligned with those needs. But even most open weight model providers do not fully reveal all of the data on which the model was initially trained.

At the Summit, Vogels also launched a new Amazon open-source AI tool designed to help researchers find relevant scientific datasets quickly.

The system connects the AWS Registry of Open Data—home to more than 1,100 datasets from organizations including NASA, NOAA, and the NIH—to AI assistants, allowing users to search using natural language rather than navigating complex data catalogs.

By enabling queries such as requests for satellite imagery or genomics datasets with specific licensing, the tool aims to replace processes that would have previously taken hours, lowering technical barriers for scientists, particularly those at under-resourced institutions, and accelerating research across fields such as climate science and public health.