---
title: "LLM prompt caching in production · a 60-80% cost cut"
description: "Prompt caching on OpenAI and Anthropic cuts LLM spend 60-80% on most production RAG systems. The 4 patterns we apply, how to measure, and 2 gotchas to know."
date: 2026-04-22
updated: 2026-04-22
author: "Dezso Mezo"
tags: "AI, LLM, RAG, Cost, Performance"
slug: llm-prompt-caching-production-savings
canonical: https://dfieldsolutions.com/blog/llm-prompt-caching-production-savings
---

# LLM prompt caching in production · a 60-80% cost cut

Prompt caching is the single biggest LLM cost lever in 2026. 4 patterns, real savings numbers, 2 gotchas worth knowing.
Anthropic added prompt caching in 2024. OpenAI followed. By 2026 it is a default on any serious LLM provider. Most teams still leave half the savings on the table because they only cache the obvious thing. Here are the four patterns that stack.

## Pattern 1 · system prompt

The easiest win. Mark the system prompt as cacheable. Every subsequent call reuses the cached prefix. Typical savings: 30-50% of total token cost on chatty support agents.

## Pattern 2 · static RAG context

If your RAG retrieves from a relatively stable corpus, the top-5 chunks are the same for many similar queries. Cache those chunks as a prefix block. Typical savings: 20-30% on top of pattern 1.

## Pattern 3 · tool schemas

Tool definitions (function schemas) are large and static across calls. Mark them cacheable. Typical savings: 10-15% on agentic workloads with many tools.

## Pattern 4 · few-shot examples

If your prompt has few-shot examples (classification, extraction), they do not change per call. Cache. Typical savings: 10-20% on extraction-heavy pipelines.

## Two gotchas

- Cache TTL is ~5 min on Anthropic, ~10 min on OpenAI. Low-traffic systems get cache misses constantly. Pre-warm with a background keep-alive if traffic is bursty.
- Prompt-caching pricing model varies · Anthropic charges ~25% extra on first write, OpenAI is free. Budget for it.

> **TIP:** Measure cost before and after per 1000 production queries. If your bill is not 60%+ lower, you missed a pattern. Every one of our 2026 RAG deployments hits or exceeds that number.

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Source: https://dfieldsolutions.com/blog/llm-prompt-caching-production-savings
Author: Dezso Mezo · Founder, DField Solutions
Site: https://dfieldsolutions.com