Prompt caching optimizes API usage by allowing resumption from specific prefixes in your prompts. This significantly reduces processing time and costs for repetitive tasks or prompts with consistent elements.
Currently, only Anthropic models support this caching feature. See Anthropic documentation for more details.

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Minimum Cacheable Length

ModelMinimum Token Length
Claude Opus 4, Claude Sonnet 4, Claude Sonnet 3.7, Claude Sonnet 3.5, Claude Opus 31024 tokens
Claude Haiku 3.5, Claude Haiku 32048 tokens

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Usage

This feature is only available through direct REST API calls. The OpenAI SDK doesn’t recognize the cache_control field.
Add the cache_control parameter to any message content you want to cache: 
import requests
import json

URL = "https://{controlPlaneUrl}/api/llm/chat/completions"
API_KEY = "TFY_API_KEY"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "X-TFY-METADATA": '{"tfy_log_request":"true"}'
}

payload = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "<TEXT_TO_CACHE>",
                    "cache_control": {
                        "type": "ephemeral"
                    }
                }
            ]
        }
    ],
    "model": "MODEL_NAME",
    "stream": True
}

response = requests.post(URL, headers=headers, json=payload)

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Monitoring Cache Performance

Monitor cache performance using these API response fields, within usage in the response (or message_start event if streaming):
  • cache_creation_input_tokens: Tokens written to the cache when creating a new entry
  • cache_read_input_tokens: Tokens retrieved from the cache for this request
Cache performance metrics