Caching LLM output looks like free latency and cost savings until a cache key collides across tenants or a popular key expires under load and stampedes your model. The senior answer is mostly about what you refuse to cache.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Caching LLM output looks like free latency and cost savings until a cache key collides across tenants or a popular key expires under load and stampedes your model. The senior answer is mostly about what you refuse to cache.
The differentiating moves are: cache keys must include every input that changes the answer (model, version, full prompt, retrieved context, tenant), and stampede protection via single-flight/locking on miss. A weak answer caches naively and leaks one tenant's answer to another or serves stale results after a prompt change. Probe with 'the customer updated their knowledge base, now what's in the cache is wrong' to test invalidation by versioned key vs TTL.
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