Why architecture matters here
Routers fail when intent classification is inaccurate. A prompt classified as "simple" but actually complex gets routed to a small model and returns nonsense. A prompt sent to an expensive model unnecessarily inflates cost.
The architecture matters because you need both an accurate classifier and a fallback ladder that catches mistakes. Combined with observability, the router learns and improves over time.
With the pieces mapped, you can plan a router that hits both cost and quality targets.
The architecture: every piece explained
The top strip is the decision path. User prompt arrives with context. Intent classifier is a tiny cheap model that predicts task type. Policy engine applies constraints (this user tier can use models up to X cost). LLM registry catalogs available models with their capabilities and costs.
The middle row is the runtime. Model selection combines capability + cost to pick the smallest capable model. Fallback ladder escalates when the primary fails or produces low-confidence output. Streaming + adapter unifies the interface across vendors. Quality gate checks output confidence before returning.
The lower rows are ops. Router observability tracks per-route accuracy, latency, and cost. A/B tests route changes before rollout. Ops budgets cost, sets latency SLOs, and holds rollback plans.
End-to-end flow
End-to-end: user asks "what's my order status?". Intent classifier: simple lookup. Policy: user is free tier, models ≤ $0.001/1k. Selection: SLM-1B. Model responds; quality gate passes; response streamed to user. Total cost: $0.0002. Compare: sending to GPT-4-scale = $0.003, 15× more. Later, user asks "analyze this contract for indemnity". Classifier: complex reasoning. Selection: LLM-70B. Fallback ladder ready if 70B errors. Quality gate confirms confident output. Observability shows router accuracy 94%, cost savings 62% vs always-large.