GraphQL vs. REST API: Which One Should You Use

The default architecture for LLM-powered features is to send every request to the most capable model available. It is the safe choice and the expensive one. A routing layer that classifies query complexity before dispatch can cut your inference bill by more than half without any degradation in output quality for the majority of requests.

Classify before you dispatch

A lightweight classifier — a fine-tuned BERT variant or even a well-prompted small model — can label each incoming request as simple, moderate, or complex with over 92% accuracy. Simple requests go to a cheap fast model. Only complex requests hit the frontier model. The classifier call costs less than 0.01% of a frontier model call.

Designing your model tiers

Three tiers covers most products: a small model for FAQ-style retrieval and slot-filling, a mid-tier model for summarisation and structured extraction, and a frontier model for reasoning-heavy tasks and anything touching money or health. Define clear capability contracts for each tier and stick to them.

Fallback and escalation logic

When a lower-tier model returns a response below your confidence threshold, escalate automatically. Log every escalation. After two weeks you will have a clear picture of where your classifier is wrong and can retrain on those failure cases. Escalation rate typically drops from 18% to under 7% after the first retrain.

Measuring quality parity

Do not trust cost savings unless you have proved quality parity. Run A/B tests where 10% of traffic still goes to the frontier model and compare your eval metrics. Once parity is confirmed at 90% routing, you have the evidence to defend the architecture to stakeholders who will inevitably ask whether you cut corners.

Author

Aarav Mehta

Writing on Developer at Technoblick