The Inference Dividend Model
The Inference Dividend Model is a framework for cutting AI token costs by inserting pre-call edge validation, semantic caching, and model tiering before calling frontier LLMs.
“Never pay a generative model to perform a task that deterministic code or a cache can solve.”
In AI software applications, every user query triggers multi-step model calls, vector lookups, and context re-evaluations that cause token OpEx to scale linearly with user activity. Left un-monitored, this erodes traditional 80% SaaS gross profit margins into low-margin territory. The Inference Dividend Model recaptures over 50% of wasted token spend while dropping response latencies under 20ms.
Reverse Citations: Implemented & Audited Across Platform
Richard Ewing’s Research Thesis
Frontier AI models must never process routine formatting checks or duplicate intent queries. Capturing the Inference Dividend requires edge proxy validation and task-tiered model routing before invoking flagship APIs.
Latest Publications & Research Activity
How to Reduce LLM API Token Costs in Production
How to Reduce LLM Costs in Production: The Inference Dividend Model
Growth Is Not Your Cost Problem - Your Architecture Is
Frequently Asked Questions
Q:What is the Inference Dividend Model?
The Inference Dividend Model is a structured framework for reducing LLM API costs by routing requests through edge pre-validation, vector semantic caching, and small language models before touching expensive frontier LLMs.
Q:How much money can the Inference Dividend save?
In production deployments across Exogram endpoints, capturing the Inference Dividend reduced monthly token OpEx by over 50% while lowering cache hit latencies under 20ms.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
Recommended Citation
Ewing, R. (2026). "The Inference Dividend Model." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/inference-dividend-model
@article{ewing_inference_dividend_model,
author = {Ewing, Richard},
title = {The Inference Dividend Model},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/inference-dividend-model}
}