Hardcoding a single LLM for every request is a common and costly mistake. Inference routing solves this by classifying each incoming query and dispatching it to the most appropriate model based on complexity. DigitalOcean's Inference Router lets developers define task categories (e.g., general FAQ, billing, technical troubleshooting), assign model pools to each, and set selection policies (lowest cost, lowest latency, best performance). The application sends all requests to a single stable endpoint using `router:<name>` as the model name, and the router handles classification and dispatch automatically. A Python support bot example demonstrates three complexity tiers being routed to different models, with the selected model name returned in every response. Additional features include fallback models for unmatched queries, model affinity headers for session-pinned routing and KV cache reuse, and an analytics tab for monitoring cost and routing distribution.
Table of contents
IntroductionKey TakeawaysWhy One Model for Everything Is a Design FlawHow DigitalOcean’s Inference Router Works Under the HoodSetting Up the Inference Router and Getting Started with Inference RouterBuilding the Support Bot: Code WalkthroughTest Router PerformanceAnalyze Router PerformanceTest Router AccuracyFAQ’sConclusionReferencesSort: