In brief
- Begin with deterministic rules before adding a learned router.
- Log requested, selected, and resolved model identities.
- Treat fallbacks as observable product behavior.
A router is a policy decision
Routing selects a model based on task characteristics, customer requirements, cost limits, or availability. The decision should be explainable enough to debug and stable enough to evaluate.
- Task class and risk
- Required capabilities and context
- Latency or spend ceiling
- Availability and regional constraints
Start with a small ruleset
A handful of named lanes is easier to validate than a complex classifier. For example: fast interactive, balanced default, and deep reasoning. Each lane should have a primary model, a tested substitute, and a reason it exists.
Record the entire selection path
Log what the application requested, what the router selected, what upstream model actually served the request, whether a fallback occurred, and why.
| Field | Purpose |
|---|---|
| requested_lane | Captures application intent |
| selected_model | Explains router choice |
| resolved_model | Supports reproducibility |
| fallback_reason | Makes reliability behavior visible |
| latency_tokens_cost | Connects choice to outcome |
Promote routing changes through evaluation gates
Replay a fixed task set, compare quality and operating metrics, canary the policy, then inspect segment-level regressions. Average wins can hide a serious failure for one customer or capability.
Frequently asked
Questions, answered plainly.
Do I need an AI model to route AI requests?+
Usually not at first. Deterministic rules are cheaper, faster, and easier to audit. Add a learned classifier only when measured complexity justifies it.
What is the safest fallback?+
A named model that supports the required capability and has passed the same evaluation set. Availability alone is not enough.
How do I know routing is helping?+
Compare quality, latency, cost, failure rate, and review burden against a single-model baseline.
Sources and next paths