Imagine you run an online marketplace of AI chatbots (LLMs) and customers can choose between several providers based on price and quality. This research treats that situation like a strategic game: providers set prices first, customers react by routing their traffic to the best-value options, and the model finds the right pricing strategy that maximizes revenue while keeping demand flowing efficiently across providers.
How to set optimal prices for online LLM services in a competitive marketplace where users can route their requests among several providers, given real demand data and service constraints.
If implemented in practice, defensibility would come from proprietary demand data and usage logs used to calibrate the Stackelberg routing model, plus embedded pricing logic inside a marketplace or platform workflow that competitors cannot easily observe.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Solving and recalibrating a Stackelberg routing game at scale as the number of providers, routes, and demand segments grows, which can make optimization computationally expensive for real-time pricing.
Early Adopters
Framing LLM pricing as a data-calibrated Stackelberg routing game is more sophisticated than simple markup or dynamic pricing; it explicitly models user routing decisions across competing LLM providers and uses real data to tune the game, enabling more realistic and strategic pricing policies in multi-provider LLM marketplaces.