Stop Paying Twice for the Same Thing

Turn repetitive prompts into serious savings Got a chunky system prompt you use over and over? Large context windows eating your budget alive? Prompt caching is your new best friend—store frequently used content once, then reference it at a fraction of the cost.

💰 The Economics of Caching

When the math actually makes sense The problem: You’re paying full price every time you send that 5,000-token system prompt—even when it’s identical to the last 100 requests. The solution: Cache it once, reuse it cheap. Here’s what you’ll save:

Provider	Cache Write Cost	Cache Read Cost	Your Savings
OpenAI	Free (automatic)	0.1x–0.5x original	50–90% off
Anthropic Claude	1.25x original (5 min) / 2x original (1 hour)	0.1x original	90% off reads*
Grok (xAI)	Free (automatic)	0.1x original	90% off
DeepSeek	Free (automatic)	0.1x original	90% off
Google Gemini	Auto (implicit) / standard input (explicit)	0.1x original (2.5+)	90% off

After the initial write cost, you’re looking at massive savings on repeated use

🎯 When Caching Makes Bank

Perfect for:

📚 Large system prompts (1000+ tokens)
🔄 Repeated context in conversations
📖 Reference documents sent with every request
🤖 Multi-turn conversations with consistent setup

Skip it for:

⚡ One-off requests
🎲 Constantly changing prompts
📏 Tiny system messages under the provider’s minimum token threshold

🛠️ Implementation That Actually Works

The Basic Setup

Cache your hefty system prompt once, reference it forever

{
  "model": "anthropic/claude-sonnet-4",
  "messages": [
    {
      "role": "system",
      "content": [
        {
          "type": "text",
          "text": "You are a senior software architect with 15 years of experience in distributed systems, microservices, and cloud architecture. When reviewing code, you focus on scalability, maintainability, security best practices, and performance optimization. You provide detailed technical feedback with specific examples and always suggest concrete improvements...",
          "cache_control": {
            "type": "ephemeral"
          }
        }
      ]
    },
    {
      "role": "user",
      "content": "Review this API design for our new user service"
    }
  ]
}

Python Implementation

Copy, paste, profit

import requests

def create_cached_request(user_message):
    url = "https://api.anyapi.ai/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    # Your expensive system prompt (cache this baby!)
    system_prompt = """
    You are an expert financial advisor with CFA certification and 20 years
    of experience in portfolio management, risk assessment, and investment
    strategy. You provide detailed analysis based on current market conditions,
    regulatory requirements, and best practices in wealth management...
    """ # Imagine this is 3000+ tokens

    payload = {
        "model": "anthropic/claude-sonnet-4",
        "messages": [
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": system_prompt,
                        "cache_control": {"type": "ephemeral"}
                    }
                ]
            },
            {
                "role": "user",
                "content": user_message
            }
        ]
    }

    return requests.post(url, headers=headers, json=payload)

# First call: Pay cache write cost
response1 = create_cached_request("Analyze my portfolio allocation")

# Subsequent calls: Pay only 10% for cached system prompt!
response2 = create_cached_request("What's your take on crypto exposure?")
response3 = create_cached_request("Should I rebalance before year-end?")

📊 Provider Deep Dive

Know your options, optimize your spend

🟢 OpenAI: Automatic & Generous

Write cost: FREE (automatic, no setup needed)
Read cost: 50% off for GPT-4o, 90% off for GPT-5 / GPT-5 Mini / GPT-5.4
Minimum: 1,024 tokens to qualify
Expiration: Typically 5–10 minutes of inactivity, always cleared within 1 hour
How it works: Fully automatic — caches the longest matching prefix starting at 1,024 tokens

🔵 Anthropic Claude: The Precision Player

Write cost: 25% premium for 5-minute TTL, 100% premium for 1-hour TTL
Read cost: 90% discount (0.1x base input price)
Limitation: Max 4 cache breakpoints per request
Expiration: 5 minutes (default) or 1 hour (extended)
Minimum tokens: 1,024–4,096 depending on model
Best for: High-frequency workflows with explicit cache control

🟡 Grok (xAI): Automatic & Simple

Write cost: FREE (automatic)
Read cost: 90% off original
How it works: Automatic — all requests benefit from caching with no configuration

🟣 Google Gemini: Dual Approach

Implicit caching: Automatic, enabled by default for most Gemini models
Explicit caching: Manual, with configurable TTL (defaults to 1 hour) + storage costs
Cost: 90% off cached tokens for Gemini 2.5+ models, 75% off for Gemini 2.0
Minimum tokens: 1,024–4,096 depending on model

🔴 DeepSeek: Automatic Savings

Write cost: FREE (automatic KV cache on disk)
Read cost: 90% off
How it works: Automatic — repeated prefixes are cached and billed at the lower rate

🎯 Pro Optimization Strategies

The Smart Workflow

# Structure your prompts for maximum cache hits
base_system = {
    "type": "text",
    "text": "Your massive, reusable system prompt...",
    "cache_control": {"type": "ephemeral"}
}

# Reuse the exact same cached content across requests
def make_request(user_input):
    return {
        "model": "anthropic/claude-sonnet-4",
        "messages": [
            {"role": "system", "content": [base_system]},
            {"role": "user", "content": user_input}
        ]
    }

Cache Hit Maximization

✅ Do this:

Keep cached content identical byte-for-byte
Bundle reusable context into cache-friendly chunks
Monitor cache hit rates in your analytics

❌ Avoid this:

Modifying cached content (instant cache miss)
Caching tiny prompts (not worth the complexity)
Ignoring expiration times (hello, surprise costs)

Cost Monitoring Magic

# Track your cache performance
def analyze_cache_savings(requests_made, avg_system_tokens, hit_rate):
    original_cost = requests_made * avg_system_tokens * INPUT_PRICE

    # Anthropic example: 1.25x write + 0.1x reads
    cache_cost = (avg_system_tokens * INPUT_PRICE * 1.25) + \
                 (requests_made * hit_rate * avg_system_tokens * INPUT_PRICE * 0.1)

    savings = original_cost - cache_cost
    print(f"You saved: ${savings:.2f} ({savings/original_cost*100:.1f}%)")

⚠️ Cache Reality Check

Remember:

🎯 Exact matching required → One character different = cache miss
⏰ Expiration is real → Plan for cache warmup in workflows
📏 Token minimums apply → Don’t cache tiny prompts
🔍 Monitor hit rates → Low hit rates = wasted write costs

When caching backfires:

Constantly changing system prompts
Infrequent API usage
Very short conversations
Prompts under provider minimums

💡 Quick Wins

Audit your system prompts → Find the chunky, reusable ones
Implement caching → Start with your highest-traffic endpoints
Monitor performance → Track hit rates and actual savings
Optimize expiration → Align cache duration with usage patterns

Ready to cut your prompt costs? Start caching the smart way with AnyAPI.

Get started

Features

Use Cases

Developer guides

Integrations

API Reference

Prompt caching

Stop Paying Twice for the Same Thing

💰 The Economics of Caching

🎯 When Caching Makes Bank

🛠️ Implementation That Actually Works

The Basic Setup

Python Implementation

📊 Provider Deep Dive

🟢 OpenAI: Automatic & Generous

🔵 Anthropic Claude: The Precision Player

🟡 Grok (xAI): Automatic & Simple

🟣 Google Gemini: Dual Approach

🔴 DeepSeek: Automatic Savings

🎯 Pro Optimization Strategies

The Smart Workflow

Cache Hit Maximization

Cost Monitoring Magic

⚠️ Cache Reality Check

💡 Quick Wins

​Stop Paying Twice for the Same Thing

​💰 The Economics of Caching

​🎯 When Caching Makes Bank

​🛠️ Implementation That Actually Works

​The Basic Setup

​Python Implementation

​📊 Provider Deep Dive

​🟢 OpenAI: Automatic & Generous

​🔵 Anthropic Claude: The Precision Player

​🟡 Grok (xAI): Automatic & Simple

​🟣 Google Gemini: Dual Approach

​🔴 DeepSeek: Automatic Savings

​🎯 Pro Optimization Strategies

​The Smart Workflow

​Cache Hit Maximization

​Cost Monitoring Magic

​⚠️ Cache Reality Check

​💡 Quick Wins

Stop Paying Twice for the Same Thing

💰 The Economics of Caching

🎯 When Caching Makes Bank

🛠️ Implementation That Actually Works

The Basic Setup

Python Implementation

📊 Provider Deep Dive

🟢 OpenAI: Automatic & Generous

🔵 Anthropic Claude: The Precision Player

🟡 Grok (xAI): Automatic & Simple

🟣 Google Gemini: Dual Approach

🔴 DeepSeek: Automatic Savings

🎯 Pro Optimization Strategies

The Smart Workflow

Cache Hit Maximization

Cost Monitoring Magic

⚠️ Cache Reality Check

💡 Quick Wins