LLMInference & InterfacesUpdated 2026.04.28

Model Routing

Also known asLLM 라우팅Query Routing모델 게이트웨이

In one line

Model routing dispatches each query to the most suitable model based on difficulty or category — the de-facto pattern for balancing cost, accuracy and latency in production AI.

Going deeper

Model routing inspects each incoming query — difficulty, domain, length — and dispatches it to the right tier: a small fast model, a big accurate one, or a reasoning-specialised one. OpenAI Router, the Anthropic model family, AWS Bedrock and Azure are all moving in this direction as a first-class feature.

It is the single most powerful lever on AI cost curves in production. Cheap model for FAQ-style traffic, capable model for nuanced policy questions, reasoning model for analytical work — average cost falls sharply while quality on hard cases stays intact.

In practice, the routing policy itself becomes the product. Misrouted cases (expensive model on trivial queries, cheap model on hard ones) compound into both cost and quality regressions, so reviewing routing logs is now a regular operational ritual.

Related terms

LLM

Test-Time Compute

Test-time compute is the paradigm of spending more inference effort per query to improve accuracy — the shift made tangible by reasoning-focused models like OpenAI o1 and DeepSeek R1.

LLM

Model Distillation

Model distillation trains a small 'student' model to imitate the outputs of a large 'teacher' model — the standard way to move expensive-model quality into a cheaper one.

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Speculative Decoding

Speculative decoding speeds up inference by letting a small 'draft' model propose several tokens at once and a big model verify them in one shot — a major lever on latency without losing quality.

LLM

A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.

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RAG

RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.

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