uncertainty-routing

Confidence-based routing reduces AI costs by automatically sending routine tasks to cheaper, smaller models and escalating only complex, low-confidence requests to larger models, achieving 10-30x cost savings while maintaining accuracy.

Task routing estimates confidence in a small model's response; if confidence falls below a threshold, the task automatically escalates to a larger model, ensuring quality while optimizing spend and processing speed.

Yes, confidence-based routing handles batch processing and high-volume, budget-conscious workflows by intelligently delegating each task based on estimated confidence, achieving 87% faster learning with significant cost reduction.

Intelligent routing separates routine tasks—routed to efficient small models—from complex tasks requiring large models, maintaining accuracy while reducing costs and latency compared to using large models exclusively.

Model routing features adaptive thresholds that adjust automatically based on task complexity and confidence estimates, with comprehensive logging and cost accounting, requiring minimal manual configuration.