AI cost router
A workload router that sends routine inference to open-model and batch lanes while reserving frontier models for high-value planning, reviews, research, and difficult reasoning.
Audience
Engineering and platform teams
Metric
Hybrid routing
Sources
3 official links
What it controls
The router turns a workload policy into model choices: cheap default lane, batch lane when latency allows, frontier reserve for judgment calls, and fallback rules when a provider is slow or unavailable.
- Routine extraction, classification, first drafts, and summaries start on open-model or batch lanes.
- Frontier models stay available for planning, architecture, complex bug fixing, code review, research synthesis, and difficult reasoning.
- Budget caps, cache expectations, latency needs, and quality bars decide when a request can escalate.
Why it matters
AI pricing gets expensive when every request is treated like a high-stakes decision. A router makes cost policy explicit before traffic reaches a model provider.
Premium models should stay available, but only for outputs that change the decision. Measure cheaper lanes before routing regular traffic to premium models.
Production shape
A practical router keeps the routing table small and auditable: default route, batch route, fallback route, and frontier escalation route. Each route should have a target price, accepted-output threshold, retry policy, and owner.
request -> workload policy -> cheap default lane
-> batch lane when latency allows
-> frontier lane for high-risk decisions
-> fallback lane when provider health changes