Stop Paying Twice for the Same AI Answer: A Simple Guide to Response Caching

This article was originally written by our colleague Vamsi H and first published on Medium, and we’re republishing it here for FastRouter community.

If you’re using ChatGPT, Claude, or any AI tool in your business, I have a question for you: How many times are you asking it the same questions?

Probably more than you think. And every time you do, you’re paying for it — again.

Let me show you a simple trick that cut my AI costs in half.

The Problem: You’re Probably Asking the Same Questions Over and Over

Think about how AI gets used in real businesses:

• Your customer support chatbot gets asked “How do I reset my password?” dozens of times every single day
• Your content tool gets asked to “Explain what SSL certificates are” by different team members

Each of these requests costs money. Even if it’s just a few cents per request, multiply that by thousands of questions every day, and you’re looking at hundreds or thousands of dollars each month.

Here’s the kicker: You’re paying full price for answers you’ve already bought before.

The Simple Solution: Remember What You’ve Already Asked

Imagine if you had a smart assistant who remembered every question you’d ever asked and the answers you got. Before bothering the expensive AI service, they’d check: “Oh, you asked something like this before! Here’s what we found out last time.”

That’s exactly what response caching does. And it’s simpler than you might think.

The Challenge: “Similar” Questions Should Get the Same Answer

You might think, “Easy! Just save questions and answers in a regular database.” But there’s a problem:

These are essentially the same question:

• “What’s the capital of France?”
• “Tell me the capital city of France”
• “Which city is France’s capital?”

A regular database looking for exact matches would treat these as three different questions and charge you three times. We need something smarter — something that understands meaning, not just exact words.

Enter Vector Databases: Storage That Understands Meaning

This is where vector databases come in. Don’t let the technical name scare you — the concept is actually pretty intuitive.

Think of it like organizing books. You could organize them alphabetically by title (exact matching), or you could organize them by topic and theme (meaning matching). Two books about gardening belong together even if their titles are completely different.

Vector databases do this for text. They convert your sentences into a kind of “GPS coordinate” in meaning-space. Sentences that mean similar things end up close together, like two coffee shops on the same street.

Here’s what happens behind the scenes:

1. “What’s the capital of France?” becomes something like a point at coordinates [0.23, 0.67, -0.45, …]
2. “Tell me France’s capital” becomes [0.24, 0.66, -0.44, …]
3. The database notices these points are very close together and says “Hey, these are basically the same question!”

How Close is Close Enough?

The database measures the distance between these points. If they’re really close (say, 95% similar or more), we can confidently say: “This is basically the same question — just return the answer we already have.”

This is called “cosine similarity” (imagine measuring the angle between two arrows — small angle means they’re pointing in almost the same direction). But you don’t need to understand the math. Just know: it figures out when two questions mean the same thing.

How It Works: The Caching Flow

Here’s the simple workflow when someone asks a question:

1. Convert the question to coordinates — Turn “What’s the capital of France?” into a vector
2. Check if we’ve seen something similar — Search the database for nearby questions
3. Found a match? — Return the saved answer instantly
4. No match? — Ask the AI, get the answer, save it for next time, and return it to the user

A Quick Code Example

Here’s what this looks like in practice with a vector database like Weaviate:

python

def get_ai_response(user_query):
# Convert question to vector coordinates
query_embedding = create_embedding(user_query)

# Search for similar questions we've asked before
results = vector_db.search_similar(query_embedding)

# If we found a similar question (95%+ match) that hasn't expired
if results and similarity >= 0.95 and not expired:
print("✅ Found in cache!")
return cached_answer

# No cache hit - ask the AI
print("🔄 Asking AI...")
response = ask_openai(user_query)

# Save for next time
vector_db.save(query_embedding, user_query, response)

return response

The beauty of this: The first time you ask “What’s the capital of France?”, it calls the AI. The second time someone asks “Tell me France’s capital city”, it recognizes the similarity and returns the cached answer instantly — no AI call needed.

The Easy Route: FastRouter

If setting up your own vector database sounds intimidating, there’s a much simpler option: FastRouter.

FastRouter is a service that sits between your app and AI providers. It handles all the caching logic for you — no databases to set up, no infrastructure to manage. You just make your API calls slightly differently:

bash

curl --location 'https://api.fastrouter.ai/v1/chat/completions' \
--header 'Authorization: Bearer YOUR-API-KEY' \
--header 'cache_key: YOUR-CACHE-KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "openai/gpt-4o-mini",
"messages": [
{ "role": "user", "content": "Tell me about physics" }
],
"max_tokens": 182,
"cache": {
"filter_on_model": true,
"expiration_time": 3600
}
}'

The expiration_time is in seconds (3600 = 1 hour). This means cached answers stay fresh for an hour, then the system will ask the AI again. The filter_on_model option ensures you don't accidentally serve a GPT-4 response when someone requested Claude.

That’s it. FastRouter handles the vector embeddings, similarity matching, and storage automatically.

When Should You Cache (and When Shouldn’t You)?

Great for caching:

• FAQs: “How do I reset my password?” gets asked constantly
• Documentation queries: “What is two-factor authentication?” doesn’t change
• Common explanations: “How does SSL work?” is always the same

Don’t cache:

• Personalized recommendations: Different for each user
• Creative writing: You want variety, not repetition
• Real-time information: Weather, stock prices, breaking news
• Ongoing conversations: Each message needs context from previous ones

My Results

After adding caching to my customer support chatbot:

• 50% fewer AI API calls — half my questions were repeats!
• Instant responses — cache hits return in 0.1 seconds vs 2 seconds for AI calls
• Significant cost savings — hundreds of dollars saved monthly

The setup took me about two hours. The savings appeared immediately.

Getting Started Today

You have two paths:

DIY Route: Set up your own vector database like Weaviate or Pinecone. Great if you want full control and are comfortable with some coding.

Managed Route: Sign up for FastRouter and let them handle everything. Great if you want to start saving money today without any infrastructure work.

Either way, start with one high-traffic use case. Maybe your FAQ bot, or your help documentation. Implement caching. Watch the costs drop. Then expand.

Ready to dive deeper? Check out the FastRouter documentation for implementation details.