Per-user LLM cost guardrail for AI SaaS

Per-user token caps, overage alerts, and model downgrade routing for SaaS with AI features

Problem

Many SaaS products bundle AI features into a flat subscription but cannot stop a single user or a runaway agent loop from burning gross margin. Multiple r/SaaS threads ask whether to set per-user hard caps, what to do on overage, whether anyone got a shocking bill from an agent loop, and how to route through cheaper models to fix unit economics.

Solution

A lightweight middleware layer: track token spend per user/tenant, support hard caps, soft alerts, and automatic downgrade to cheaper models, and return preset responses or queue when over budget. Includes a budget dashboard and anomaly alerts.

Users

Indie SaaS developers shipping AI features, small backend teams, AI product operators

MVP

SDK/proxy to meter tokens; per-user budgets and hard caps; overage email alerts; simple downgrade rules; single-page cost dashboard

Revenue

Subscription by monitored volume or MAU, metered after a free tier; self-hosted license buyout

Why now

AI features are becoming table stakes, yet most small teams only see totals in provider dashboards; per-user cost control is thin and an indie can ship it quickly