Your team is paying $40k/month on LLM tokens: most calls share a long system prompt + 8k tokens of few-shot examples. How does prompt caching help and what's the expected savings? · Zenaique

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You're an MLOps engineer reviewing a $40k/month LLM token bill. Most calls share the same system prompt (~500 tokens) + ~8k tokens of stable few-shot examples + ~2k tokens of per-query content. Explain how prompt caching works, what specifically becomes cacheable, the expected savings, and at least one operational gotcha that could prevent realizing them.

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Quick reference

TL;DR

Cache the 8.5k stable prefix at one-tenth input price; effective input cost drops about seventy percent, taking the $40k bill to roughly $21k after caveats.

Memory aid

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Easy to grasp

Imagine paying a tutor to read your textbook out loud every time you have a question. Most of the textbook is the same; only the question changes. Prompt caching lets the tutor say 'I remember the textbook, just ask your question.' You still pay full price for the question itself and for the tutor's answer, but the textbook is now nearly free per session. For an app paying forty thousand dollars a month where most of the input is the same long set of examples and rules, that move alone cuts the bill in half. The catch is the tutor only remembers the textbook if you bring it in the exact same words every time and ask the next question soon enough.

Key concepts

Prompt Caching Inference Optimization

Detailed answer & concept explanation

~5 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

3 min: prefix memoization + the $40k math + byte-exact and TTL gotchas + cache_control markers + hit rate as the production metric.

Real-world examples

Real products, models, and research that use this idea.

Anthropic's prompt caching docs publish exactly this kind of system plus few-shot scenario as the canonical Claude Opus 4.7 use case, citing roughly ninety percent input cost reduction on the cached portion.
OpenAI's GPT-5.5 automatic prefix caching fires on prefixes longer than 1024 tokens and surfaces cached_prompt_tokens in usage telemetry, which production teams treat as the hit-rate metric.
Cursor and similar coding agents stack system, tool definitions, and project context in the stable region of every call so each follow-up turn hits the cache and only the fresh diff pays full input rate.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow would you measure cache hit rate after rollout?

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QWhat if the few-shot examples drift in production?

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QWhen does prompt caching beat model routing as a cost lever?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Counting output tokens in the projected savings; caching is input-side only and output is always billed at full rate.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.