LLM router

An LLM router is a software layer that directs each incoming request to the most appropriate large language model (LLM) based on criteria such as cost, task complexity, latency requirements, or organizational policy, rather than sending every request to a single model.

In AI-powered customer service, not every interaction carries the same requirements. A routine order-status lookup demands speed and low cost; a multi-step complaint resolution may require a more capable model with a larger context window. Without deliberate routing, teams either over-spend by using a frontier model for every request, or accept quality degradation by forcing complex tasks through a cheaper one. An LLM router resolves this tension by making the selection decision automatically, at inference time, on a request-by-request basis.

How an LLM router works

A router sits between the application layer and the model layer. When a request arrives, the router evaluates it against a set of rules or a classifier and selects the target model. The response is then returned to the application as if a single model had handled it. Most routers also implement fallback logic: if the primary model is unavailable or returns an error, the request is automatically retried against a secondary model.

Common routing strategies include:

• Semantic routing: A lightweight classifier reads the query and maps it to a model profile based on topic, intent, or expected complexity. This approach works well when the task distribution is predictable and the classification overhead is low relative to the routing benefit.
• Cost-based routing: The router assigns a cost budget to each request type and selects the cheapest model that meets the minimum capability threshold. This is often the first strategy operations teams implement when they begin managing inference time costs at scale.
• Latency-based routing: Time-sensitive channels such as live chat or voice route to faster models regardless of cost, while asynchronous channels route to more capable but slower ones. This directly affects customer-perceived latency and is especially critical for AI voice channels where delays above roughly 300ms are perceptible.
• Policy-based routing: Certain request types, such as those involving regulated data, financial advice, or escalation-adjacent topics, are always sent to an approved model regardless of cost or latency, allowing teams to enforce compliance rules at the infrastructure layer.
• Fallback routing: When a primary model returns a low-confidence response or an error, the router automatically escalates to a stronger model, limiting quality failures without human intervention.

Why LLM routing matters for customer experience

LLM routers are part of the broader practice of AI agent orchestration, which coordinates the tools, models, and memory systems an agent relies on during a conversation. Routing decisions affect every downstream metric: a slow model on a synchronous channel inflates average handling time; an underpowered model on a complex case raises escalation rate; an expensive model on every request makes unit economics unsustainable at volume.

Routing also interacts with prompt engineering. Different models respond differently to the same prompt, so routing pipelines typically maintain separate prompt templates per model to maintain output consistency. The model context protocol (MCP) is one emerging standard for normalizing how context is packaged and passed to models, which simplifies the work of maintaining multi-model pipelines. Teams that do not account for this often see quality variance between models that is more attributable to prompt mismatch than to model capability differences.

Implementing and monitoring an LLM router

Effective routing requires ongoing measurement. Teams should track model selection distribution over time, alongside quality and cost metrics per route, to verify that the classifier is behaving as intended and that model capability has not drifted. A route that made sense when a cheaper model lacked a capability may become the wrong default after that model is updated. Routing rules should be treated as configuration that is reviewed on the same cadence as model updates.

When evaluating LLM routing vendors and frameworks, OpenAI's routing and model selection documentation outlines how cost and capability trade-offs differ across their model tiers, which provides a useful baseline for designing routing criteria. For teams building on top of agentic systems, Decagon's guide covers how routing fits into the broader architecture of production-grade AI agents.

For a deeper dive, download Decagon's guide to agentic AI for customer experience.