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How do prompt caching and semantic caching cut LLM cost and latency, and what are the risks?

Caching is one of the biggest LLM cost levers, but 'cache the response' is naive for a non-deterministic system. The signal is distinguishing prompt (prefix) caching from semantic caching and knowing when each is safe.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Caching is one of the biggest LLM cost levers, but 'cache the response' is naive for a non-deterministic system. The signal is distinguishing prompt (prefix) caching from semantic caching and knowing when each is safe.

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Next in this trackHow does LLMOps differ from traditional MLOps, and how do you version and manage prompts in production?Next in this trackWhat is LLM routing (model cascades / semantic routing), and how do you implement it?Next in this trackHow do you detect data drift and concept drift in production, concretely?

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