Prompt Caching Guide: Anthropic & OpenAI Latency & Cost Optimization (2026)

Large Language Model API costs scale linearly with input context size. Prompt Caching optimizes enterprise LLM deployments by storing invariant system instructions, API schemas, and vector context blocks in high-speed GPU memory caches across sequential requests.

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1. How Prompt Caching Works Under the Hood

```

[ Request 1: Invariant Prefix (2,000 Tokens) + User Input A ] ---> Full Attention Compute (100% Cost)

[ Request 2: Invariant Prefix (2,000 Tokens) + User Input B ] ---> CACHE HIT! (50% Cost, 80% Faster)

```

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2. Structuring Prompts for Maximum Cache Hits

To maximize cache hits across API pipelines, strictly structure messages into two distinct segments:

1. Top-Level Invariant Segment: Place static system directives, database schemas, and few-shot examples at the top of the prompt payload.

2. Bottom Dynamic Segment: Append variable user inputs and dynamic timestamps at the bottom.

To monitor, track, and optimize prompt caching performance across your AI infrastructure, integrate your pipelines with PromptOptima.

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Frequently Asked Questions

What is Prompt Caching in Anthropic Claude and OpenAI?

Prompt Caching allows foundation model APIs to store previously processed prompt prefixes in memory. Subsequent requests sharing the same prefix bypass initial attention computation, reducing cost and latency.

How much cost and latency savings does Prompt Caching provide?

Prompt Caching reduces input token cost by 50% and improves time-to-first-token (TTFT) latency by up to 80%.

What is the minimum token threshold for Anthropic Prompt Caching?

Anthropic Claude requires a minimum prefix block length of 1,024 tokens to trigger prompt caching.