Cost Savings Analysis Intermediate

Understanding the financial impact of prompt caching is essential for making informed architectural decisions. This lesson breaks down real-world cost savings with concrete calculations for different usage patterns.

Savings by Provider

Provider Cache Write Cost Cache Read Cost Net Savings (after 10 hits)
Anthropic (Claude) 1.25x base price 0.1x base price ~88% on cached tokens
OpenAI (GPT-4o) 1.0x (no premium) 0.5x base price ~50% on cached tokens

Worked Example: Chatbot with System Prompt

Consider a customer support chatbot with a 3,000-token system prompt handling 1,000 conversations per day:

Calculation (Anthropic Claude Sonnet) 
# Without caching:
Input cost per request = 3,000 tokens * $3.00/MTok = $0.009
Daily cost = 1,000 requests * $0.009 = $9.00
Monthly cost = $9.00 * 30 = $270.00

# With caching (assuming 95% cache hit rate):
First request (cache write) = 3,000 * $3.75/MTok = $0.01125
Cached requests (950)       = 3,000 * $0.30/MTok = $0.0009 each
Cache misses (49)           = 3,000 * $3.00/MTok = $0.009 each

Daily cost = $0.01125 + (950 * $0.0009) + (49 * $0.009)
           = $0.01125 + $0.855 + $0.441
           = $1.31
Monthly cost = $1.31 * 30 = $39.30

# SAVINGS: $270 - $39.30 = $230.70/month (85% reduction)

Break-Even Analysis

How many cache hits do you need to recover the cache write premium?

Anthropic Break-Even: With a 25% write premium and 90% read discount, you break even after just 1-2 cache hits. Every subsequent hit saves 90%.
OpenAI Break-Even: With no write premium and 50% read discount, you save from the very first cache hit. However, the per-hit savings are lower (50% vs 90%).

When Caching Saves the Most

📈

Long System Prompts

The longer your cacheable prefix, the more tokens benefit from the reduced rate. A 5,000-token system prompt saves more than a 1,000-token one.

🔁

High Request Volume

More requests = more cache hits. Batch processing and high-traffic applications see the biggest absolute savings.

💬

Multi-Turn Conversations

Each turn adds to the cached prefix. By turn 5, most of the input is cached, making long conversations very cost-efficient.

📄

RAG with Stable Docs

When retrieved documents remain the same across multiple questions, caching the document context yields significant savings.

When Caching Helps Less

  • Unique prompts: If every request is completely different, there is nothing to cache
  • Short prompts: Below the minimum threshold (1,024 tokens), caching does not apply
  • Infrequent requests: If requests are more than 5-10 minutes apart, cache entries expire
  • Dynamic prefixes: If the beginning of your prompt changes each time, prefix matching fails
← OpenAI Caching Implementation →

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