Cutting LLM Costs 70% Without Cutting Quality

We've reviewed dozens of enterprise LLM deployments where the pilot was a success and the invoice was a crisis. Costs that look trivial at 100 requests a day become budget items at 100,000. The good news: most deployments can cut spend 50–70% without users noticing.

Route by task difficulty

The single biggest lever. Most production traffic is easy — classification, extraction, routine drafting — and doesn't need a frontier model. A router that sends easy tasks to a small model and hard reasoning to a frontier model typically cuts costs 40%+ on its own.

Cache aggressively

• Prompt caching for long, repeated system prompts and documents
• Semantic caching for frequently-asked questions with stable answers
• Response reuse across users where permissions allow

Right-size your prompts

Bloated prompts are pure waste. Audit token usage per request type; we routinely find 30% of tokens are boilerplate that can be compressed or moved into cached prefixes.

Distill where volume justifies it

For high-volume, narrow tasks, fine-tuning a small model on frontier-model outputs delivers comparable quality at a tenth of the cost. The break-even is usually around a few hundred thousand requests per month.

Make cost a first-class metric

Track cost per resolved task alongside quality metrics, alert on anomalies, and review weekly. Teams that treat inference spend like cloud spend — with FinOps discipline — never get surprised.