Model Routing

What is Model Routing?

An architectural approach where a controller directs specific queries to different language models based on performance, cost, or task requirements

In other languages

한국어모델 라우팅

입력된 작업의 난이도나 유형에 따라 가장 적합한 AI 모델을 선택하여 요청을 전달하는 기술이다.

日本語モデルルーティング

リクエストの難易度やコスト、速度などの条件に基づき、複数のAIモデルの中から最適なモデルへ動的に処理を振り分ける仕組み。

Related Terms

• Model routerAn intelligent system that analyzes an input prompt and dynamically directs it to the most appropriate or efficient model for that specific task.
• LLM routerSoftware layer that selects among language models for a request based on criteria such as cost, latency, or capability
• Model cascadingA method that routes inputs among models according to factors such as complexity, cost, accuracy and latency.
• Model orchestrationTechnique for coordinating multiple AI models or agents to handle tasks based on suitability
• Model portabilityDesign principle that allows software systems to switch among AI models with limited rebuilding
• Large Language Model as a judgeAn architectural pattern in AI development where a secondary model is used to evaluate, grade, or filter the responses generated by a primary model for quality control.
• Mixture of Experts (MoE)A machine learning architecture that breaks a large model into specialized sub-networks (experts) and uses a router to activate only the most relevant ones for each input.
• MoEA machine learning architecture that uses a gating network to select specific sub-models, called experts, to process different types of data, increasing efficiency by only activating a fraction of the total parameters.
• ModelOpsModelOps is a set of practices for managing the full lifecycle of machine learning models, including development, deployment, monitoring, and ongoing maintenance.
• MambaA sequence modeling architecture that uses state-space models to achieve linear scaling in processing speed and memory usage compared to traditional architectures.
• Model EndpointA specialized network gateway or URL that exposes a trained machine learning model, allowing external applications to send data and receive predictions in real-time.
• Tool CallMechanism where an AI model programmatically invokes external functions or APIs to perform specific operations