Most AI-native products today are overpaying for LLM usage because they send too many requests to expensive frontier models, even when the task does not require them.
In this session, I’ll share how LLM routing works in practice: how to classify prompt complexity, route requests across models, and balance cost, latency, and quality without hurting the user experience.
I’ll also cover the common traps teams run into when building routers, including over-routing to premium models, bad offline evaluation, feedback loops, and the gap between benchmark accuracy and real production quality.
The goal is to give builders a practical framework they can use to decide when to use cheaper models, when to escalate to stronger models, and how to measure whether the routing system is actually saving money while preserving output quality.
Dor Amir is building Nadir getnadir.com, an LLM router that helps AI-native products reduce model costs by routing each request to the right model based on prompt complexity, quality requirements, latency, and cost.
Dor currently works at Dropbox on the agentic AI team, building AI agents that improve Dropbox experiences. Before Dropbox, he worked as a Senior Machine Learning Engineer on Amazon’s Personalization team, where he focused on applied ML systems. Earlier in his career, he worked across data science, machine learning, and software engineering roles, including ML leadership at Guesty and recommendation ML at Fiverr.
Dor’s work sits at the intersection of machine learning, LLM infrastructure, and production AI systems. His current focus is helping teams build practical AI agents and LLM routing systems that reduce cost, improve latency, and preserve quality in real-world applications.
