AetherGate
Routing Guide · Updated October 2026

LLM routing: model selection strategies for multi-provider AI

LLM routing is the process of deciding which model or provider should handle a request. A production router can use explicit rules, capability requirements, cost ceilings, latency targets or fallback state instead of sending every request to the same model.

Key takeaways

  • Start with explicit routing rules before adding opaque automatic selection.
  • Route on capabilities first, then optimize for cost or latency inside the eligible set.
  • Keep the requested model, selected provider and routing reason visible in telemetry.
  • Treat fallback as a separate reliability policy rather than silently changing models.

The main LLM routing techniques

Rule-based routing maps known request types to known models. Capability routing filters by requirements such as tool use, context size or multimodal support. Cost routing selects the least expensive eligible option, while latency routing prefers faster providers or regions.

Quality routing can use evaluations or a classifier to choose among models, but it is harder to debug. Most production systems benefit from deterministic constraints before any learned or probabilistic router is allowed to choose.

  • Rule-based routing for predictable workloads.
  • Capability routing for tools, vision, context or structured output.
  • Cost-aware routing inside a permitted model set.
  • Latency-aware routing for interactive applications.
  • Provider routing for regional or availability constraints.
  • Fallback routing after defined transient failures.

Multi-LLM routing architecture

The router should sit behind a stable gateway interface so applications do not need provider-specific logic. A routing policy receives request metadata, evaluates eligible targets, chooses one and records the reason for that choice.

Keep routing configuration versioned and observable. When output quality or cost changes, operators need to know whether the model changed, the provider changed or the routing policy changed.

How to evaluate an LLM router

Test the same workload against a fixed-model baseline and the routing policy. Measure cost, latency, error rate, quality and the percentage of requests sent to each model.

A router is only useful if the savings or reliability gains are larger than the quality variance and debugging complexity it introduces.

Related gateway comparisons

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Frequently asked questions

What is LLM model routing?

LLM model routing is the selection of a model or provider for each request based on rules or signals such as capability, cost, latency, quality or availability.

What is the best LLM routing strategy?

There is no universal strategy. A strong default is capability filtering first, deterministic business rules second, and cost or latency optimization only within the models that can safely perform the task.

Is routing the same as fallback?

No. Routing selects the preferred path before the request is sent. Fallback is a reliability policy that activates after a selected path fails or becomes unavailable.