Prompt caching reduces API input costs by up to 90% and cuts latency by 85% for repetitive tasks. Both Anthropic and OpenAI support it natively as of early 2026, making it a mandatory architectural pattern for any production AI application.

If your app sends the same system instructions, RAG documents, or few-shot examples with every API call, you are burning money. Caching stores this prefix on the provider's servers.

How Prompt Caching Actually Works

When you send a request, the API checks if the initial chunk of your prompt matches a previously stored block. If it does, the model skips processing those tokens.

According to OpenAI's caching release notes, a 100,000-token document that takes 8 seconds to process initially will process in under 1 second on subsequent cached calls.

Step-by-Step Implementation for Anthropic

Implementing caching requires a minor structural change to your API payload. You must explicitly tell the API which blocks of text to hold in memory.

  1. Identify static content: Move your heavy, unchanging data (system prompts, large PDFs) to the very beginning of the messages array.
  2. Add the cache control block: Append the cache_control: {\"type\": \"ephemeral\"} parameter to the specific message object you want to store.
  3. Monitor usage: The API response will now include a cache_creation_input_tokens and cache_read_input_tokens object in the usage statistics.

Cost Savings Breakdown

To illustrate the financial impact, consider a customer support bot that loads a 50,000-token manual for every user query.

  • Without Caching: 1,000 queries = 50,000,000 input tokens. Cost: $150.00.
  • With Caching: The first query costs $0.15. The next 999 queries read from the cache at $0.30 per 1M tokens. Total Cost: $15.15.
  • Net Savings: 89.9% reduction in API spend.

Frequently Asked Questions

How long does the cache last?

Anthropic's ephemeral cache lasts for 5 minutes of inactivity. OpenAI automatically clears cached prefixes after 5 to 10 minutes of no use. Both reset the timer every time the cache is hit.

Are there extra fees for storing the prompt?

Anthropic charges a 25% premium to write to the cache initially ($3.75 per 1M tokens), but reading from it is a 90% discount. OpenAI does not charge a premium to write, applying a straight 50% discount on cached reads.

Does caching degrade output quality?

No. The output is mathematically identical. Prompt caching is a systems-level optimization, not a model compromise. The LLM behaves exactly as if it read the full text fresh.

Your Next Step

Audit your application's API logs. Identify the largest static block of text you send repeatedly (usually a system prompt or RAG context). Update your API headers to enable caching and check your billing dashboard tomorrow.