How to Cut Your AI Costs in Half While Doubling Performance

Source: Dev.to

Traditional caching breaks the moment someone rephrases a question. A user asks “What are your business hours?” and gets a response. Five minutes later, another user asks “When are you open?”—semantically identical, but different words; the cache misses entirely. This hidden tax on AI applications causes LLM costs to balloon because standard caching only catches exact string matches.

The Limitations of Exact‑Match Caching

Most caching systems work like this:

1. Hash the request.
2. Check if that exact hash exists in the cache.
3. Serve the cached response if it does.

This works brilliantly for static assets or database queries where requests are identical. LLM requests, however, are rarely identical. Consider these variations:

• “What’s the refund policy?”
• “How do I get a refund?”
• “Can I return this product?”
• “What is your return policy?”

A human instantly recognizes these as the same question. Traditional caching sees four different requests and makes four separate API calls, each costing $0.002–$0.03 depending on the model and token count.

For an AI‑powered customer‑support system handling 10,000 queries daily, the waste compounds quickly. If 30 % of queries are semantic duplicates (a conservative estimate), that’s 3,000 unnecessary API calls every single day.

Semantic Caching with Bifrost

Bifrost, an open‑source LLM gateway, solves this problem with semantic caching—understanding meaning rather than matching text. Early production deployments show cost reductions between 40 %–60 %, with some use cases seeing savings up to 85 %.

How It Works

When a request arrives:

1. Generate an embedding for the prompt using a small, fast model (e.g., text-embedding-3-small).
2. Search the vector store for cached entries with high semantic similarity.
3. If similarity exceeds the configured threshold (typically 0.8–0.95), return the cached response.
4. If no match exists, call the LLM and cache both the response and its embedding.

The key insight: two semantically similar prompts produce similar embedding vectors, even if the exact words differ. Vector similarity search finds these near‑matches in milliseconds.

Example

• User asks: “What are your business hours?” → embedding generated, response cached.
• Later, another user asks: “When are you open?” → embedding is mathematically similar; Bifrost serves the cached response in .