Start with workloads, not model rankings
A single global model leaderboard is a weak routing policy. Classification, extraction, summarization, code generation, long-context synthesis, and customer-facing advice fail in different ways. Define task families and success checks before assigning tiers.
For deterministic tasks, success may be valid JSON, exact labels, or field-level accuracy. For open-ended generation, the quality bar may combine a rubric, style constraints, safety checks, and human sampling. Routing becomes defensible when the pass condition is attached to the workload rather than inferred from price.
Build a conservative decision ladder
A useful policy starts with hard exclusions: pinned models, minimum context windows, required modalities, tool support, latency ceilings, data residency, and provider restrictions. Only models that pass those filters enter the price comparison.
The router can then choose among qualified candidates using historical pass rate, effective cost, latency, and current provider health. A small model that frequently escalates or requires retries may be more expensive than its list price suggests.
- Filter by capability and policy.
- Estimate task family and complexity.
- Choose the lowest-cost qualified route.
- Validate output with cheap deterministic checks.
- Escalate once when a check fails.
- Record the full decision and realized cost.
Measure matched quality
FrugalGPT demonstrated that cascades can reduce cost substantially on benchmark workloads while preserving a target quality level. RouteLLM similarly evaluates routers by the tradeoff between cost and response quality. The important phrase is matched quality: savings without a defined comparison point are not evidence.
Use a golden set drawn from real traffic, then add continuous samples from production. Segment results by team, route, language, and risk level so an average score does not hide a weak cohort.
Make rollback part of the design
Every enforcement policy should have a pass-through mode and a fast return to monitor-only operation. Track rolling quality, schema validity, refusals, and application errors. When a service-level threshold is breached, stop optimizing that cohort automatically and preserve the evidence for review.
Frequently asked questions
How much can LLM routing save?
Published research reports large benchmark savings, including up to 98% in FrugalGPT and about 85% at 95% of frontier quality in RouteLLM. Actual savings depend on traffic mix, model prices, quality thresholds, retries, and cacheability.
What requests should never be routed automatically?
Pin regulated, safety-critical, contractually constrained, or poorly evaluated workloads until you have route-specific evidence and an approved fallback.
Should routing use an LLM classifier?
It can, but deterministic metadata and simple rules are often cheaper and easier to audit. If an LLM classifier is used, meter its cost and evaluate its errors.
Sources and further reading
FrugalAI uses primary documentation and published research where possible. Product capabilities and prices can change; verify vendor details before procurement or production changes.