Caching Economics: Building Cost-Efficient LLM Pipelines

Introduction

As developer workflows shift from simple chatbot interactions to autonomous agent loops, prompt length is exploding. Every cycle in an agent loop requires sending the conversation history, available tools, system guidelines, and user history back to the model.

If you are building an agent that runs 10 loops to complete a task, you are repeating the same context blocks (system rules, tool schemas, and history) over and over. This results in:

1. High token bills.
2. Unnecessary latency overhead.

This is where Prompt Caching changes the economics of AI engineering.

Caching Savings: Gemini vs Claude vs GPT-4o

Different model providers offer different prompt caching pricing profiles:

• Google Gemini: Automatically caches prompts above 32k tokens. Cached input is billed at a 75% discount ($0.3125 / M tokens instead of $1.25 on Gemini 1.5 Pro).
• Anthropic Claude: Supports manual cache breakpoints. Billed at a 90% discount for cache hits ($0.30 / M tokens instead of $3.00 on Claude 3.5 Sonnet).
• OpenAI GPT-4o: Automatically caches prompts on matches. Billed at a 50% discount ($2.50 / M tokens instead of $5.00).

Live Caching Calculator

Below is an interactive caching calculator built directly into this article. Adjust the sliders to see how your input volume, output volume, and cache match rate affect your month-end API expenses.

Implementing Caching in Code

To leverage prompt caching in Anthropic Claude, you simply specify cache control breakpoints in your messages payload:

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are an advanced marketing performance optimizer. Here is our 10,000 line database...",
            # Highlight this block for caching
            "cache_control": {"type": "ephemeral"}
        }
      ],
    messages=[
        {"role": "user", "content": "Analyze the latest campaign report."}
    ]
)

By adding the "cache_control": {"type": "ephemeral"} metadata, Claude bakes the preceding system context into local memory. Future calls matching this block will hit the cache, lowering costs by up to 90%.