80% Cost Reduction with Context Caching: Implementation Guide
Maximizing Anthropic/OpenAI Context Caching features. Revealing system prompt design tips.
I couldn't believe my eyes when I saw the API bill. ¥2 million per month.
As the number of stores using our service grew, API costs increased proportionally. At this rate, the business wouldn't be sustainable.
When the engineering team analyzed the billing breakdown, we discovered something.
"Teaching the Same Thing Every Time"
Every time our AI received a customer question, we were sending it all the store information. Store name, address, phone number, business hours, menu, FAQ—about 3,000 tokens in total.
With 1,000 inquiries per day, that's 3 million tokens. Most of which was the same information every time.
Every time we asked AI "What time does our store close?", we explained everything about the store from scratch. Like a workplace where you introduce yourself from the beginning every morning.
"Learn It Once, Remember It"
The Context Caching feature from Anthropic and OpenAI solves exactly this problem.
Store unchanging information (basic store info, menu, FAQ) in cache. For each customer question, only add the "changing information."
The AI responds while "remembering" the cached information.
80% Cost Reduction
Here are the numbers after implementing caching.
Cache hit rate: 85%. That means 85% of requests no longer needed to resend store information from scratch.
Monthly API costs went from ¥2 million to ¥400,000. An 80% reduction.
"Order" Matters
One important note: cache only works for portions that match from the beginning of the prompt.
We redesigned our prompts to put unchanging information first, and changing information (like today's availability) at the end.
Don't teach AI the same thing over and over. This simple principle brought us an 80% cost reduction.