Model Economics
LLM Routing Strategies: Cost, Quality, Latency, and Availability
Compare rule-based, cost-aware, latency-aware, and quality-aware model routing with a practical decision framework.
What you will learn
- Define the product objective before selecting a routing strategy.
- Use hard constraints before weighted optimization.
- Log policy version and route inputs so decisions are explainable.
Before you start
- Basic HTTP and API knowledge
Leave with a concrete implementation checklist and a testable starting point.
Key takeaways
- Define the product objective before selecting a routing strategy.
- Use hard constraints before weighted optimization.
- Log policy version and route inputs so decisions are explainable.
Choose the objective first
Routing is an optimization problem. A support classifier may prioritize cost and latency, while a coding agent may prioritize quality and context handling.
Write the objective and hard constraints before choosing a routing algorithm. Otherwise the router will optimize a metric that does not match the product requirement.
Strategy comparison
Rule-based routing is predictable and easy to audit. Cost-aware routing is useful for high-volume workloads. Latency-aware routing reacts to live conditions but needs good telemetry. Quality-aware routing can use evaluation scores, but those scores must represent your real tasks.
Hybrid policies usually work best: hard constraints first, then a weighted choice among eligible models.
A safe first version
Start with a small allow-list and explicit rules. Add live signals only after you can explain current decisions from logs. Every route should be reproducible from the request metadata and policy version.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Define quality, cost, latency, and availability constraints.
- 2
Create an eligible-model allow-list.
- 3
Add one optimization signal at a time.
- 4
Evaluate route outcomes on representative traffic.
Copy-ready example
eligible = models where endpoint_supported && account_enabled
route = argmin(cost) subject to quality >= threshold and p95 <= budgetFrequently asked questions
Should routing always choose the cheapest model?
No. Cheapest is only correct when it satisfies the quality, latency, and availability constraints.
Sources
- EasyAI documentationSource checked 2026-08-27