Jev Cuts AI Decision Costs 100x And Vercel, Cloudflare Rushed To Add It

Most of what an AI agent asks a frontier model to do is not writing. It is choosing. Which tool to call next, whether to retry, whether a command is safe to run. Jev, released September 15 by TypeSafe AI, answers those questions in under half a second for $0.042 per million input tokens and nothing on output. Within three days Vercel, Cloudflare, LangChain and Langfuse had added it to their stacks.

DCVC led a $40 million seed round to back founder Diogo Almeida, who helped build the instruction-following research behind ChatGPT at OpenAI before spending two years in stealth with co-founders Erik Gafni and Sasha Sheng. James Hardiman, general partner at DCVC, described the bet as turning capable models into components developers can build into products at scale. Agent vendors are the buyers. Decagon, which raised $250 million at a $4.5 billion valuation in January, runs support agents whose every routing, scoring and escalation step is currently a language model call. Move those steps to a model with free output and the margin expands for every agent company at once, while the lab owning the decision layer sits under all of them. TypeSafe named the model after William Stanley Jevons, whose paradox observed that cheaper coal produced more coal consumption, and expects intelligence to follow the same curve.

The mechanics are simple enough to explain in one sentence. Code sends a block of state and typed questions, and Jev returns a Choice among up to 255 options, a Score on an ordered scale, or a Noul, a yes-no probability, with calibrated confidence attached to each. Every question in a request is evaluated in parallel. TypeSafe trained it with a method it calls Reinforcement Learning for Calibrated Decisions, so confidence tracks accuracy and code can act automatically above a threshold and route to a human below it. Schema matching is guaranteed, which means an agent deciding between four tools cannot invent a fifth.