Definition
Large Language Model (LLM) Caching stores previously computed LLM responses or intermediate computations to avoid rerunning expensive inference for identical or similar requests.
Running LLM inference at scale is expensive in both time and money. A single request to a GPT-4 class model with 4,000 input tokens and 512 output tokens costs a few cents and takes 500 milliseconds to 2 seconds for p50 latency. That might seem small, but consider production scale.
At 1,000 queries per second (QPS), those costs explode. You're burning through tens of thousands of dollars per day just on model inference. For a consumer application at 500 QPS serving customer support queries with 1,000 input tokens and 300 output tokens, the math is brutal: roughly $0.019 per request translates to $9,000 per day and over $3 million per year.
Why Caching Helps:
Many production workloads have natural repetition. Enterprise tools see the same Frequently Asked Questions (FAQs) repeatedly. Chat systems receive similar variations of common questions. Financial analysis tasks often follow structurally similar patterns even when details differ.
Instead of calling the expensive LLM every single time, you store results from previous requests. When an identical or similar request arrives, you return the cached response in under 5 milliseconds p99 from an in-memory store. This is 100x to 400x faster than waiting for model inference.
Impact of 30% Cache Hit Rate
With just a 30 percent cache hit rate, you immediately cut 30 percent of your LLM costs. For that customer support application spending $9,000 per day, that's $2,700 saved daily or about $1 million annually. Latency for cache hits drops from 700 milliseconds p50 to under 5 milliseconds, dramatically improving user experience for a third of your traffic.
✓LLM inference at scale costs tens of thousands of dollars per day: a 1,000 QPS service can spend over $3 million annually
✓Production workloads often have 20 to 40 percent logical repetition in queries like FAQs, common chat patterns, or similar analysis tasks
✓Caching returns stored responses in under 5 milliseconds p99 versus 500 to 2000 milliseconds for fresh model inference, a 100x to 400x speedup
✓Even a modest 30 percent cache hit rate cuts both costs and latency by 30 percent for those requests, saving millions annually at scale
1Enterprise customer support receiving the same 'how do I reset my password' question hundreds of times daily can cache the response instead of calling GPT-4 each time
2Financial analysis tool that generates earnings summaries sees similar query patterns across different stocks, allowing cached plan structures to be reused
3Internal HR chatbot answering policy questions caches responses for frequently asked topics like benefits enrollment or vacation policies