Scaling LLM Applications with Caching
Design architecture and caching strategies to scale LLM applications from hundreds to millions of requests.
Scaling LLM applications from low to high volume requires architectural changes beyond simple code optimization. Caching becomes critical: a 10% cache hit rate on a 1,000-request-per-day application has minimal impact, but on a 10M-request-per-day application, it can reduce API costs by millions. Scalable architectures employ distributed caching (Redis, Memcached), cache warming (pre-loading frequently accessed data), cache hierarchies (local + distributed), and geographic distribution. Load increases present challenges: cache coherency across distributed instances, cache stampede (thundering herd) when popular cache entries expire, and handling graceful degradation when cache becomes a bottleneck. Advanced techniques include predictive cache loading and adaptive TTL based…
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