DEEP EXPLANATION

Prompt Caching and KV Cache: Cost and Latency Wins (ANSWERED)

Project BasedLLMsMedium15 min read

Project question on prefix/prompt caching and KV cache reuse — TTFT optimization and provider billing implications.

TL;DR — Quick Answer

KV cache stores attention key/value tensors from prior tokens so decode steps don't recompute the full prefix — critical for autoregressive speed. Prompt caching (provider feature) reuses computation for identical long static prefixes (system prompt, docs) across requests — lower TTFT and discounted input tokens on some APIs. Production wins: stable system prompts, factor RAG context placement, warm repeated templates, and session-level cache where supported. Invalidate cache on prompt version change.

The Interview Question

Explain prompt caching and KV cache in LLM inference. How do they reduce cost and latency in production?

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KV CachePrompt CachingLatencyCostInferenceOpenAIAnthropicGoogle

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