Prompt Caching Strategies: How to Cut Your LLM API Bills by 50%

As developer workflows shift toward long-context systems — such as uploading entire codebases, schemas, or book manuscripts into model prompts — API costs have spiked. To make these workflows economically viable, major AI providers have introduced **Prompt Caching**. When implemented correctly, prompt caching can slash your input token costs by **50% to 90%** and drastically reduce latency.

What is Prompt Caching?

Normally, when you send a prompt to an LLM API, the server must process (or "tokenize and prefill") the entire input text from scratch, charging you the full input token rate every single time. With prompt caching, the provider saves the processed state of your prompt's prefix. If your subsequent API calls start with the exact same prefix, the model retrieves the processed state from the cache, bypassing the prefill compute phase.

How Caching Works Under the Hood

Prompt caching relies on **prefix matching**. The cache can only be matched from the very beginning of the prompt. If you change a single character at the start of your prompt, the entire cache invalidates, and you will be charged the full rate.

For example, consider the following prompt structure:

[STATIC CONTEXT: System prompt + Database schema + API documentation]
[DYNAMIC INPUT: User's question: "How do I query users by name?"]

Because the `STATIC CONTEXT` is at the beginning, it can be cached. The `DYNAMIC INPUT` at the end changes every time, which does not invalidate the cache for the prefix.

Three Strategies to Maximize Cache Hits

1. Sequence Your Prompts Carefully (The "Static-First" Rule)

Always place your most static, heavy content at the absolute top of the prompt. Put highly dynamic fields (like timestamps, chat history, or user queries) at the very bottom.

Bad Structure (No Cache Hits):

Today's Date: July 8, 2026.
System Prompt: You are a SQL helper.
Database Schema: [10,000 tokens of tables]
User Question: [User text]

Why it fails: Changing the date at the very top invalidates the entire cache for the database schema on subsequent days.

Good Structure (High Cache Hits):

System Prompt: You are a SQL helper.
Database Schema: [10,000 tokens of tables]
Today's Date: July 8, 2026.
User Question: [User text]

2. Explicit Caching in Anthropic (Claude API)

Unlike OpenAI and DeepSeek, which cache automatically, Anthropic's API requires developers to explicitly specify which blocks of the prompt should be cached using the cache_control property. You can place up to 4 cache breakpoints in a single request.

Here is an example using the Python SDK:

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a specialized code reviewer...",
            # This block will be cached
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Here is the codebase: [100,000 tokens of code]",
                    # This huge block will also be cached
                    "cache_control": {"type": "ephemeral"}
                },
                {
                    "type": "text",
                    "text": "Find the memory leak in auth.py."
                }
            ]
        }
    ]
)

3. Keep Chat History Orderly

In multi-turn chat applications, the entire conversation history grows over time. Because the historical turns are static, they can be cached. Ensure that you append new messages to the end of the array, rather than prepending them, to preserve the prefix cache match.

Measuring the ROI of Caching

To verify if your caching strategies are working, monitor the response usage metadata returned by the API. The API returns tokens broken down by category:

• Anthropic: Look for input_tokens vs. cache_read_tokens and cache_creation_tokens.
• DeepSeek/OpenAI: Look for cached_tokens under the prompt token breakdown.