Great walkthrough on implementing dynamic routing with Model Serving and AI Gateway. The idea of routing based on query complexity and model capability really hits the mark, especially as teams start scaling real GenAI workloads beyond single-model experiments.
The practical examples are what make this shine. Showing external judges in the routing flow and the validation-endpoint pattern helps move this from theory into something teams can actually implement. That part will resonate with folks trying to productionize responsibly.
One question I’m curious about: have you seen teams close the loop with feedback over time? For example, refining routing decisions based on observed performance metrics, cost, latency, or quality outcomes in production. It feels like there’s a natural evolution where routing logic gets smarter as you learn which models truly perform best for specific classes of queries.
Thanks for sharing both the architecture patterns and the code — this is exactly the kind of content that helps teams level up from demos to durable systems.
