AI routing dynamically selects which LLM handles each request based on query complexity, cost constraints, or specialized capabilities. Simple queries go to cheaper models while complex ones use premium models.
Routing Strategies
- Complexity-based: Route by query difficulty
- Task-based: Route by task type (code, chat, analysis)
- Cost-based: Route by budget constraints
- Latency-based: Route by speed requirements
Monitoring Routing Decisions
- Track routing accuracy vs quality outcomes
- Monitor cost savings from routing
- Alert on routing failures or fallbacks
- Compare model performance by query type
Practical example
Short classification tasks use one evaluated path while complex requests are escalated to another.
What to check
Record the routing rule or model version, selected destination, failed routes, and quality by task group.
Limits and tradeoffs
Cheaper calls can increase total cost if incorrect routing produces retries or manual rework.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.