Does routing make results inconsistent?
Not if every route is evaluated against the same test set and outputs follow a fixed format.
AI Orchestration · Model routing
Model Routing: The Right Model for Each Task
No single model is best for every task. Routing rules select models deliberately instead of by habit.
Routing criteria
- Task type: extraction, reasoning, drafting, coding, classification
- Required quality measured on your test set
- Latency and throughput
- Cost per request
- Data class and allowed processing location
Routing strategies
Static rules
Each use case is assigned a model. Simple and transparent.
Cascades
A small model answers first; uncertain cases escalate to a larger one.
Fallbacks
If a provider fails or rate-limits, a second model takes over.
Evaluating routing decisions
Compare models on the same test set per use case and repeat the comparison when providers release new versions.
Example routing policy
| Use case | Primary model | Fallback | Reason |
|---|---|---|---|
| Email classification | Small, fast model | Medium model | High volume, simple task |
| Contract analysis | Large reasoning model | None - queue for retry | Quality over speed |
| Internal knowledge answers | Medium model with retrieval | Large model | Balance of cost and accuracy |
| Confidential HR data | Privately hosted model | Human handling | Data protection |
Common pitfalls
- Choosing the most expensive model for every task by default
- Switching models without re-running the evaluation
- Ignoring rate limits and provider outages
- Not logging which model produced which answer
Frequently asked questions
Can confidential data be routed to public models?
Only if your policies and contracts allow it. Routing rules should enforce data classes automatically.