Controlling AI API Spend in a Next.js 15 Micro-SaaS with Cordis

On a Sunday morning at 3:17 AM, an individual's credit card faced a $1,400 charge due to a user repeatedly attempting to generate content on a Next.js 15 micro-SaaS application. The user's mobile device experienced a network interruption, causing the browser to repeatedly retry the request. The serverless handler, designed to handle each disconnection and spawn new API calls, eventually exhausted the monthly quota, resulting in a barrage of 429 errors.

Micro-SaaS projects often face financial strain because a single AI inference call can be prohibitively expensive. The root of the issue lies in the absence of a robust boundary separating user intent from execution orchestration, retry mechanisms, and financial tracking. To address this, the author emphasizes the importance of making AI execution observable, cacheable, and budget-controlled without embedding billing logic within user interface components.

Cordis, a Meta-Framework of Spatiotemporal Composability, is presented as a tool to manage this complexity. However, the author cautions that Cordis is still in its early stages, with its core APIs subject to frequent changes, making it an unreliable standalone solution. Instead, the author suggests treating Cordis as an internal orchestration boundary, isolated behind a strict application adapter.

The author outlines a four-tier architecture for a production-ready AI request flow: the Next.js 15 Route Handler, Application Service, Cordis Orchestration Boundary, and AI Gateway and Accounting Routes. Each tier plays a specific role in validating user input, translating business logic, managing temporal execution semantics, and handling billing and auditing.

By isolating these components and adhering to a stable run() contract, developers can ensure reliable AI execution while maintaining control over costs and preventing unexpected service disruptions.