Cost control for LLM features in production
Budgets, caching, and model tiers so AI remains a product cost—not a surprise invoice
LLM features fail quietly on the balance sheet: a helpful assistant that burns the margin on every support ticket. Cost control is product design—quotas, caching, and model choice—not a finance spreadsheet after the fact.
Put a budget next to the feature flag
Define a per-user or per-tenant daily token budget before launch. Soft-limit with a clear UI; hard-limit before you protect margin. Unknown spend is not “agile.”
Simple daily budget gate
Loading code…Cache and shrink before you scale models
Cache embeddings for stable documents. Cache identical system prompts. Summarize long threads before sending them. Smaller context beats a larger model for many tasks—and it is cheaper to measure.
Tier models on purpose
Route cheap classification to a small model; reserve frontier models for high-value drafts. Make the router explicit in code so costs are reviewable in PRs.
Model tier by job
Loading code…Instrument like any other dependency
Log tokens in, tokens out, model id, feature name, and tenant. Alert when spend velocity spikes. A weekly cost review belongs next to error-rate review—same ceremony, different graph.
Final thoughts
Treat LLM spend as a product constraint: budgets at the gate, cache before scale, tier models by job, and instrument usage like any paid dependency. Surprise invoices are a design failure.
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