LLM cost control is a product feature

Why this matters

If your unit economics are powered by tokens, you are running a software business and a commodities desk at the same time.

Token prices fluctuate. Usage spikes. A single bad prompt can burn through your monthly budget in hours. Most teams discover this the hard way—after the invoice arrives.

Cost control isn't a nice-to-have. It's a product feature that determines whether your AI product is viable at scale.

Three levers that work

1. Route by difficulty

Not every request needs your most powerful model.

**Easy tasks** (classification, simple extraction): cheap, fast models

**Medium tasks** (summarization, structured generation): mid-tier models

**Hard tasks** (complex reasoning, creative generation): premium models

Build a classifier that routes requests to the cheapest model that can handle them. Start simple—even a keyword-based router beats sending everything to GPT-4.

2. Cache what users repeat

You'd be surprised how often users ask the same questions. Cache aggressively:

**Embeddings and retrieval results** — same query = same vectors

**Deterministic transformations** — formatting, extraction from stable sources

**Stable tool outputs** — API responses that don't change frequently

A 30% cache hit rate can cut your spend by 30%. Measure it.

3. Reduce tokens by design

Tokens are your raw material. Use fewer:

**Strict contracts** — don't let the model ramble; define output schemas

**Structured outputs** — JSON mode, function calling, typed responses

**Summaries and state snapshots** — compress context instead of replaying full history

**Explicit step limits** — cap the number of reasoning steps

Put budgets in the code

Don't just monitor—enforce. Build these into your system:

**Per-request budgets** — fail fast if a single request gets too expensive

**Per-user budgets** — prevent abuse and runaway usage

**Per-tenant monthly caps** — for B2B, protect yourself from outliers

**Alerting when budgets spike** — catch problems before they become invoices

The path forward

If you want to cut LLM spend without breaking quality, my 5-day Cost & Reliability Tune-Up installs:

Routing logic with model tiering

Response caching layer

Budget enforcement and alerts

Observability to track spend by feature

Book a call to discuss your current spend and where the savings are.