LLM Cost Control for Production Apps: Budgets, Caching, and Kill Switches

An LLM feature without a cost envelope is a liability. Production teams need hard limits per user, per tenant, and per day — plus visibility when a prompt change doubles spend overnight. Treat tokens like any other metered cloud resource.

Instrument every call: model, input/output tokens, latency, route, tenant, and feature flag. Alert on spend anomalies, not only on HTTP 500s. If you cannot answer “what did this feature cost yesterday,” you are not ready to scale it.

Cut waste before you cut quality. Cache deterministic system prompts, shrink retrieved context, and refuse oversized uploads. Prefer smaller models for classification and routing; reserve frontier models for the final answer when evidence is ready.

RAG cost is often retrieval + generation. Cap top-k, dedupe chunks, and skip generation when retrieval confidence is low. Streaming does not reduce tokens — it only improves UX. Tool-calling loops need a maximum step count or they will burn money in silence.

Add a kill switch and a degraded mode: FAQ-only answers, human handoff, or “try again later.” Rate-limit anonymous traffic harder than authenticated users. Log prompt versions so you can roll back a costly change in minutes.

If your AI bill spiked after a launch or PoC, request a free audit. I will map call paths, propose budgets, and a fixed-price plan to put controls in place without gutting the product.