One of the more useful shifts in conversational AI architecture over the past year is the widespread adoption of model routing — using a lightweight classifier to direct queries to the most cost-appropriate model rather than sending everything through a single large one.
The basic routing logic
A typical customer-facing conversational system handles a mix of query types: simple FAQs, moderate clarification requests, and genuinely complex reasoning tasks. A 70B parameter model handles all three, but charges the same inference cost for each. A 7B model handles the first two adequately and costs a fraction of the price. Routing is the mechanism that sends each query to the right tier.
What routing classifiers look like in practice
The classifier itself is often a small fine-tuned model or even a rule-based system using embedding similarity to predefined query categories. Teams at several mid-size SaaS companies have described routing 60 to 70 percent of their conversational traffic to smaller models after auditing their query logs and finding that the majority of interactions were straightforward enough to handle without a large model.
Recent tooling that makes this easier
LiteLLM added routing and fallback logic that works across providers. RouteLLM, released by researchers earlier this year, specifically addresses quality-preserving routing with minimal accuracy loss. Both reduce the engineering effort of implementing a tiered model strategy from scratch.
The cost reduction from routing is proportional to how varied your query complexity is. For most business applications, that variation is significant enough to make routing worth the setup time.