Your SaaS Is an Insurance Product: A Modeling Framework

TL;DR

This paper presents a modeling framework that treats capped-usage SaaS products like insurance, using actuarial science principles to improve pricing and reserve adequacy analysis.

Contribution

It introduces a practical operational framework for capped-usage SaaS pricing based on frequency-severity decomposition and reserve modeling, filling a gap in current practice.

Findings

Mapped SaaS to insurance models using observable subscription tiers

Grounded the framework in canonical health-insurance economics

Demonstrated divergence from traditional unit economics with a worked example

Abstract

Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice.