Model routing is the decision layer that chooses where an AI request runs. The simplest strategy is a fixed provider and model. More advanced systems add traffic weights, workload rules, provider health, cost or latency signals. The right strategy is the simplest one that makes an important production trade-off explicit and observable.
Key takeaways
- Start with deterministic rules before introducing dynamic routing complexity.
- Every routing decision should remain visible in request telemetry.
- Fallback is a reliability policy, not a substitute for measuring model quality differences.
Static routing is a valid production strategy
A fixed model per workload is easy to reason about, test and audit. If your application has stable requirements, static routing may be better than a dynamic system that creates hidden variability.
The key is centralizing the rule so model changes do not require editing multiple services and so the request record still captures which provider and model served the call.
Weighted routing helps with migrations and experiments
Weighted traffic can move a percentage of requests to a new provider or model while the rest stays on the current path. That is useful for comparing latency, reliability and cost on real production traffic.
Treat the experiment like a release: define success metrics, keep cohorts identifiable and make rollback simple.
Cost-aware routing needs quality boundaries
Choosing the cheapest available model can reduce spend while harming the workflow. Cost-aware routing should start with a set of models that are acceptable for the task, then optimize within that boundary.
Measure cost per successful outcome, not only price per token. A cheaper model that creates more retries or failures can be more expensive at the workflow level.
Latency-aware routing needs enough traffic to trust the signal
Provider latency changes over time, but routing on a noisy short-term metric can cause instability. Use recent health and latency data with sensible windows and minimum sample sizes.
Keep a deterministic fallback when dynamic data is unavailable. A routing system should degrade toward predictable behavior, not invent a new decision when telemetry is incomplete.
Fallback must be explicit
Fallback defines what happens when the preferred route cannot serve a request. Specify which errors qualify, how many attempts are allowed and whether the alternate model is capability-equivalent for the workload.
Record the original target and the final route. Without that evidence, teams cannot explain reliability incidents, quality changes or the extra spend created by retries.
FAQ
Common questions
What is the simplest LLM routing strategy?
A fixed model and provider per workload. It is deterministic, testable and often the right starting point. Add dynamic routing only when cost, latency or reliability requirements justify the extra complexity.
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