LLM Routing
Choosing the right model for each task based on quality, cost, latency, energy, and policy.
What is LLM Routing?
LLM routing is how enterprise AI economics actually work. Routing pushes classification and summarization to smaller models, reserves frontier models for hard reasoning, and respects policy (some tasks cannot leave a boundary). See the LLM Routing pillar and the SEEMR architecture for the VDF AI implementation.
Why it matters for on-premise & regulated AI
Routing is the mechanism that makes an on-premise AI estate economical: routine tasks go to small local models, hard tasks to larger local models or — where policy allows — external frontier APIs. A policy-aware router also enforces compliance at runtime: workloads tagged with residency or confidentiality constraints are only ever routed to approved, in-boundary models. Cost optimization and compliance enforcement become the same routing decision.
Read the full guide: LLM Routing — in-depth article →
Related terms
Putting LLM Routing to work?
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