Definition. Inference architecture is a production economics problem: every request has a quality requirement, latency budget and compute cost that must remain compatible as usage grows.
Start with a workload profile
Measure request volume, context size, output length, concurrency, p50/p95 latency, failure rate and cost per successful task. Averages alone can hide the workloads that dominate user experience or infrastructure spend.
Route by task requirements
Use the least expensive model or execution path that passes the task's evaluation threshold. Routing can also reserve slower, more capable inference for ambiguous or high-risk cases while keeping routine work fast.
Control the context and queue
Context windows, retrieval payloads and concurrency can dominate inference cost and latency. Bound them deliberately, add caching where data is reusable, and make backpressure visible rather than letting queues grow silently.
Observe the economics continuously
Track cost per completed task, not only cost per token. Tie infrastructure metrics back to user outcomes so optimization does not accidentally increase retries, escalations, or failure recovery work.
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