How to Cut LLM API Costs by 80% (Without Hurting Quality)

TL;DR

Six levers, ranked by return: route easy requests to small models (50–80% savings), cache prompts (up to 90% on repeated context), shorten your system prompt, batch non-urgent work (50% discount on most APIs), cache identical responses, and measure cost per feature so you know where the money actually goes.

Every LLM feature starts the same way: prototype on the biggest model, ship it, then open the invoice three months later. We've audited enough production AI bills to say this confidently: most teams overpay by 5–10x.

The six levers, ranked

Lever	Typical saving	Effort
Model routing	50–80%	Days
Prompt caching	Up to 90% on cached tokens	Hours
Shorter system prompts	10–30%	Hours
Batch API for async work	50% flat	Days
Response caching	20–60%	Days
Cost-per-feature monitoring	Enables all of the above	Days

1. Route requests to the cheapest capable model

Not every request needs your best model. Classification, extraction, formatting, and short summaries run fine on models that cost 10–20x less. The pattern: try the small model first, escalate to the big one when confidence is low or the task is genuinely hard. In practice 60–85% of traffic never needs escalation.

2. Use prompt caching

If every request re-sends the same system prompt, tool definitions, and context documents, you're paying full price for tokens the provider has already seen. Prompt caching cuts the cost of that repeated prefix by up to 90%. It's often a one-line change.

3. Audit your system prompt

We regularly find 3,000-token system prompts where 800 tokens do the work. Every token in the prompt is paid on every request. Cut instructions the model ignores anyway, and move rarely needed rules into retrieval.

4. Batch everything that isn't interactive

Nightly enrichment, tagging, translations, report generation — none of it needs a real-time endpoint. Batch APIs cost about half as much for the same output.

5 & 6. Cache responses and measure per feature

Identical inputs deserve cached outputs, especially for search-style features. And without cost attribution per feature you can't see which product surface burns the budget — the fix is a logging middleware, not a platform.

We run this exact optimization as a fixed-scope engagement — usually 1–2 weeks, and it typically pays for itself within a quarter. See our AI & LLM Integration services, or start with our LLM integration guide.