ecomode

Cost-aware model routing reduces AI inference token costs by preferring cheaper model tiers for routine agent workloads. It dynamically delegates tasks to lower-cost models and escalates only when higher fidelity is explicitly required.

Tier-aware model selection works by routing tasks to cheaper model tiers first, then applying escalation rules to upgrade to higher tiers only when necessary. This preserves safety constraints while actively reducing token usage and compute latency.

Yes, dynamic delegation supports routing typical agent workloads including planning, exploration, and execution. It applies explicit tier preferences to ensure cost-sensitive inference is used for these tasks before escalating to more expensive models.

The best way to implement tiered fallback is by applying explicit tier preferences alongside escalation rules in your routing logic. This ensures tasks attempt cheaper model tiers first and fall back to higher tiers only if fidelity requirements are unmet.

You should avoid cost-aware routing when tasks consistently require maximum fidelity without allowing tiered fallback, or when strict safety constraints demand immediate high-tier processing without initially attempting cheaper model tiers.