How to cut LLM API costs 40–70% without losing quality
AI API spend is the fastest-growing quiet line item in software P&Ls. The fix is rarely 'use AI less' — it's five engineering levers, ranked here by effort-to-savings ratio.
First, get attribution — you can't cut a number nobody owns
Most teams see one monthly bill and no breakdown. Before optimizing anything, tag every API call by feature and endpoint, and log tokens in/out. A week of data answers the only question that matters: which 20% of calls cause 80% of spend? In our audits it's usually one or two features — often internal tooling nobody remembers shipping.
Cost per call = (input tokens × input price) + (output tokens × output price). Output tokens typically cost 3–5× input tokens — remember that; it drives lever #4.
Lever 1 — Model routing: stop paying flagship prices for commodity tasks
Classification, extraction, formatting, routine summarization — smaller models handle these at parity for a fraction of the price. Flagship models earn their cost on genuine reasoning, hard synthesis, and high-stakes output. Route by task: small model by default, automatic escalation on low confidence or explicit triggers. Routing alone typically saves 25–40% and is invisible to users when the escalation rules are honest.
Validate with paired evaluation: run both models on a sample of real traffic, compare outputs against your quality bar, then commit. 'Feels worse' isn't data.
Lever 2 — Prompt caching: stop re-buying the same tokens
If every request re-sends the same system prompt, tool definitions, and reference documents, you're paying full price for identical tokens thousands of times a day. Prompt caching (supported by major providers) makes repeated context dramatically cheaper — commonly ~90% off cached input. Structure prompts cache-first: stable content at the top, variable content at the bottom, and cache breakpoints deliberate.
Caching is the highest ROI-per-hour change on this list: often one day of prompt restructuring for a 20–30% total bill reduction on context-heavy workloads.
Levers 3–5 — Context discipline, output control, batching
Context management: stop sending entire documents and full chat histories. Retrieve the relevant chunks; summarize old turns; cap the window. Half of most context is ballast. Output control: since output tokens are the expensive ones, set max lengths, request terse formats (JSON over prose), and stop generating what you'll truncate anyway. Batching: for anything asynchronous — enrichment, backfills, nightly jobs — batch APIs run at ~50% off. If nobody is waiting on the response, it should be batched.
Stacked honestly, the five levers land most teams at 40–70% savings with unchanged output quality — the audited constraint in Nexargate's AI token optimization service. Under $2K/month of spend? Run this checklist yourself. Above $5K? The audit pays for itself in the quick-wins phase, and pairs naturally with broader AI consulting.
Key takeaways
- check_circleAttribute spend per feature first; one or two features usually drive most of it.
- check_circleRoute by task: small models by default, flagships only where reasoning pays.
- check_circlePrompt caching: stable context first, variable last — ~90% off repeated input.
- check_circleOutput tokens cost 3–5× input: cap lengths, prefer JSON, stop over-generating.
- check_circleBatch everything asynchronous for ~50% off. Then lock savings in with per-feature dashboards.
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