Originally published on AI Tech Connect.
What prompt caching is and why it saves so much Every time you call an LLM API, the provider's inference servers process every token in your prompt from scratch — parsing, attending, computing. That processing is where most of the cost lives. Prompt caching lets the provider store the key-value (KV) state for a portion of your prompt so that subsequent calls with the same prefix can skip the expensive recomputation entirely. The result is dramatic. Anthropic reports up to 90% cost reduction and 85% latency reduction for long prompts when cache hits occur. Across all major providers, 2026 benchmarks show prompt caching reduces API costs by 41–80% and improves time-to-first-token (TTFT) by 13–31%. The savings are so substantial because most production LLM workloads have a large, static…
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