Blog · August 3, 2026 · 7 min read
Cutting LLM Costs Without Cutting Quality
A practical playbook for LLM cost engineering: model tiering, caching, prompt budgets, batching, and the metrics that tell you when quality actually dropped.
LLM bills grow like cloud bills did in 2015: invisibly, then suddenly. On products serving hundreds of active customers, token spend is a first-class engineering concern. This is the playbook I apply on production systems like TryCook.ai — ordered by effort-to-impact ratio.
1. Tier your models per step
Map every LLM call in your product and ask: does this step need judgment, or just competence? Classification, extraction, reformatting, and routing run fine on models that cost 10-30x less than frontier ones. In most agent pipelines, fewer than 20% of calls genuinely need the top model. Tiering alone usually halves the bill.
2. Cache aggressively — at three levels
- Provider prompt caching: keep system prompts and few-shot examples byte-stable so cached-token pricing kicks in (often 10x cheaper on the cached prefix).
- Semantic caching: identical questions deserve identical answers. Hash normalized inputs; on hit, skip the call entirely.
- Artifact caching: if the model generated a summary of document X yesterday, store it. Regenerate on change, not on request.
3. Give every prompt a token budget
Context windows invite hoarding. Set an explicit input budget per call site and enforce it in code — retrieve less, truncate history with summaries, strip boilerplate. Output matters too: verbose chain-of-thought you never read is pure spend. Ask for terse structured output and reconstruct prose only where a human reads it.
4. Batch and debounce
Not everything needs to be synchronous. Nightly enrichment, bulk classification, and re-scoring jobs belong on batch APIs (typically 50% cheaper) or queued off-peak. Debounce user-triggered calls: if someone edits a field five times in a minute, process the final state, not all five.
5. Watch quality with evals, not anecdotes
Cost cuts fail when quality silently drops. Guard every downgrade with an eval set: run the cheap model against your graded cases, ship only if the pass rate holds. This is the same eval discipline from running agents in production — cost work is just another change that needs regression tests.
The order of operations
- 01Instrument per-feature token spend (you can't cut what you can't see)
- 02Tier models per call site
- 03Stabilize prompts for provider caching
- 04Impose input/output token budgets
- 05Move async work to batch pricing
- 06Re-run evals after every change
Teams that do this treat tokens like any other COGS line — measured, budgeted, and owned. Teams that don't end up rationing features their margins could have funded.
Haider Farooq is an AI engineer and data scientist based in Lahore, Pakistan — core engineer on TryCook.ai, developer at Aligno, and creator of MarkSafe.net. He builds agentic AI systems, RAG pipelines, and automation for teams worldwide. Work with him.
RELATED WORK