Your LLM Cache Has a 70% Hit Rate. So Why Isn’t Your Bill Going Down?
Whether you understand that a high cache hit rate does not always mean high cost savings, and can trace where token usage and LLM costs are actually coming from.
Interviewer:
Your semantic cache has a 70% hit rate, yet the LLM bill barely decreases.
Question: What would you investigate to determine why caching isn't translating into meaningful cost savings?
Answer:
1. Check whether cache hits actually skip the LLM call.
2. Measure tokens saved per cache hit, not just hit rate.
3. Check if cached queries are mostly small, low-cost requests.
4. Verify whether large requests are missing the cache.
5. Check cache similarity thresholds and false misses.
6. Check if prompt or context changes prevent cache reuse.
7. Measure cache latency, storage, and embedding costs.
8. Compare total LLM calls and token usage before and after caching.
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What the interviewer is really testing
A 70% semantic-cache hit rate does not automatically mean a 70% reduction in LLM cost.
The key is to trace whether those hits actually eliminate expensive model calls and how much token usage each avoided call represents.
If I saw a 70% cache hit rate but almost no reduction in the LLM bill, I would investigate:
Verify that cache hits actually bypass the LLM.
A cache hit should normally return the stored response without sending the request to the model. If the application still makes an LLM call after a hit, the cache is not delivering its expected cost benefit.Measure tokens avoided per cache hit, not just the hit percentage.
A 70% hit rate can still produce limited savings if the cached requests contain very few tokens while the remaining 30% of requests contain most of the expensive token usage.Check which requests are being cached.
Determine whether cache hits are concentrated among short, inexpensive requests while long, expensive requests continue to miss the cache.Analyze cache misses by token and dollar cost.
Look at input tokens, output tokens, model used, and estimated cost for both hits and misses. The important metric is not simply "how many requests hit?" but "how much LLM spend did those hits eliminate?"Investigate similarity thresholds and false misses.
A threshold that is too strict can cause semantically equivalent requests to miss the cache. However, lowering the threshold increases the risk of returning an incorrect cached answer, so it must be tuned against both savings and answer quality.Check whether changing context prevents cache reuse.
User-specific context, conversation history, retrieved documents, tenant information, model settings, or other request metadata may make otherwise similar queries unsuitable for reuse.Account for the cache's own costs.
Every request may still require embedding generation and vector-search infrastructure. Storage and lookup costs also need to be included when calculating the net savings.Compare before-and-after economics.
I would compare:Total LLM requests
Input tokens
Output tokens
LLM spend
Cache hits and misses
Embedding cost
Cache/vector-store cost
Total system cost
The final metric should be net cost savings, not cache hit rate alone.
Strong interview summary
A 70% hit rate is only meaningful if those hits eliminate expensive LLM calls. I would therefore trace the request path, measure tokens and dollars avoided per hit, identify where the expensive requests are missing, evaluate similarity thresholds and context-dependent misses, and subtract the cost of embeddings and cache infrastructure. Finally, I would compare total LLM and infrastructure spend before and after caching.