Prompt Caching: The LLM Optimization That Halves Costs and Latency

Prompt caching is the single architectural optimization that fundamentally transforms LLM economics—not a tuning trick but a direct consequence of how Transformer attention operates. By reusing cached key-value vectors from stable prefixes, developers can reduce inference costs by 50–90% while cutting time-to-first-token by 3–10×, making it the difference between profitable and unsustainable AI products. Understanding provider-specific implementations (Claude's explicit markers, DeepSeek's disk-backed approach, Gemini's hourly storage fees) has become essential for cost engineering decisions at scale.