Large Language Model (LLM) costs are skyrocketing for many businesses. One company found its API bill growing 30% monthly, not due to traffic, but because users ask the same questions in different ways. The solution? Semantic caching – a technique that dramatically reduces redundant LLM calls by understanding meaning, not just matching words.
The Problem with Exact-Match Caching
Traditional caching relies on exact query matches. This works if users phrase questions identically, but most don’t. Analysis of 100,000 production queries revealed:
- Only 18% were exact duplicates.
- 47% were semantically similar (same intent, different wording).
- 35% were entirely new.
That 47% represents a massive cost opportunity. Each slightly rephrased query triggered a full LLM call, generating an almost identical response. Exact-match caching simply missed these savings.
How Semantic Caching Works
Instead of hashing query text, semantic caching uses embeddings. These are numerical representations of meaning. The system finds cached queries within a similarity threshold:
The core idea: embed queries into vector space and find near matches, instead of relying on exact text.
The Threshold Problem: Precision vs. Recall
The similarity threshold is critical. Too high, and you miss valid cache hits. Too low, and you return incorrect responses. A threshold of 0.85 might seem reasonable, but testing revealed problems:
For example, a query asking about subscription cancellation might incorrectly match with a cached response about order cancellation.
The optimal threshold varies by query type:
- FAQ-style questions (0.94): High precision needed to avoid damaging trust.
- Product searches (0.88): More tolerance for near matches.
- Support queries (0.92): Balance between coverage and accuracy.
- Transactional queries (0.97): Extremely low tolerance for errors.
Latency Overhead: Is It Worth It?
Semantic caching adds latency (embedding + vector search). Measurements showed:
- Query embedding: 12ms (p50) / 28ms (p99)
- Vector search: 8ms (p50) / 19ms (p99)
- Total cache lookup: 20ms (p50) / 47ms (p99)
The overhead is negligible compared to the 850ms average LLM call time. At a 67% hit rate, the net result is a 65% latency improvement alongside the cost reduction.
Cache Invalidation: Keeping Responses Fresh
Cached responses become stale. Product information changes, policies update, and answers expire. The following strategies are crucial:
- Time-based TTL: Expire content based on its volatility (e.g., pricing updates every 4 hours).
- Event-based invalidation: Invalidate when underlying data changes (e.g., when a policy is updated).
- Staleness detection: Periodically check if a cached response is still accurate by re-running the query and comparing embeddings.
Production Results: Real-World Impact
After three months, the results were significant:
- Cache hit rate: Increased from 18% to 67%.
- LLM API costs: Decreased by 73% (from $47K/month to $12.7K/month).
- Average latency: Improved by 65% (from 850ms to 300ms).
- False-positive rate: Remained low at 0.8%.
This optimization delivered the highest return on investment for production LLM systems. Careful threshold tuning is vital to avoid quality degradation.
Semantic caching is not a “set it and forget it” solution. Continuous monitoring and adjustment are essential.
Key Takeaway: Implementing semantic caching requires careful planning, but the cost savings and performance gains make it a worthwhile investment for businesses relying on LLMs.
