What is Prompt Caching?

What Is Prompt Caching?

Prompt Caching is an optimization available through AI provider APIs that stores the processed version of prompt content that stays the same across multiple requests, so the AI does not need to re-process it each time. When your application sends an API request to an AI model, it typically includes a system prompt, instructions, context documents, and the user's actual question. In many applications, everything except the user's question stays the same across hundreds or thousands of requests. Prompt caching recognizes this and processes the repeated content only once, reusing it for subsequent requests.

Think of it like a restaurant kitchen. Without caching, every order requires starting from scratch -- preheating the oven, preparing the base sauce, and then making the dish. With caching, the base sauce is prepared once and kept warm, so each new order only requires the final unique preparation steps.

How Prompt Caching Works

When you make an API call to an AI model, the prompt text goes through a processing step called prefill where the model computes internal representations of your input. This prefill step consumes computational resources and time. With prompt caching:

1. First request: The full prompt is processed normally, and the processed representation of the static portion is stored in a cache
2. Subsequent requests: The API recognizes that the static portion of the prompt matches a cached version, skips the prefill computation for that portion, and only processes the new, unique part of the request
3. Cache management: Caches typically have a time-to-live (TTL), expiring after minutes to hours of inactivity, and are managed automatically by the API provider

Anthropic offers prompt caching on the Claude API, allowing businesses to cache system prompts, tool definitions, and large context documents. OpenAI provides similar caching capabilities for repeated prompt prefixes. Google has implemented caching features in its Gemini API as well.

Why Prompt Caching Matters for Business

Significant cost reduction For applications that include long system prompts or large context documents with every API call, prompt caching can reduce input token costs by up to 90 percent on cached portions. If your AI-powered customer service bot includes a 5,000-token system prompt and company knowledge base with every customer interaction, caching means you pay full price once and a fraction for every subsequent interaction.

Faster response times Skipping the prefill computation for cached content means the AI model starts generating its answer sooner. For customer-facing applications where response speed directly impacts user experience, this latency improvement is immediately noticeable. Reductions of 50 percent or more in time-to-first-token are common with effective caching.

Enabling richer context at manageable cost Without caching, businesses face a trade-off: include more context for better answers but pay higher costs per query. Caching largely eliminates this trade-off for repeated context. You can include comprehensive product catalogs, policy documents, and company guidelines in every prompt without the cost scaling linearly with volume.

Scalable AI applications For businesses in Southeast Asia scaling AI applications across thousands of daily interactions -- customer service bots, document processing pipelines, sales assistants -- prompt caching transforms the economics from concerning to comfortable. A Grab-scale operation handling millions of queries would see massive savings.

Key Examples and Use Cases

Customer service chatbots: A chatbot for a regional bank includes the same system prompt, FAQ database, and company policies with every customer interaction. Caching this static content means the bank only pays full price once per cache period, reducing per-interaction costs dramatically.

Document analysis pipelines: A legal tech company in Singapore processing hundreds of contracts daily against the same regulatory framework can cache the regulatory reference documents, paying only for the unique contract content in each request.

E-commerce assistants: An online marketplace operating across ASEAN can cache product catalogs and return policy documentation, enabling AI shopping assistants to provide informed answers at a fraction of the uncached cost.

Internal knowledge bases: Companies using AI to help employees search internal documentation can cache the entire knowledge base context, making every employee query faster and cheaper.

Getting Started

1. Identify repetitive prompt content: Audit your AI API calls to find the content that stays the same across requests -- system prompts, instructions, reference documents, and tool definitions are common candidates
2. Structure prompts for caching: Place static content at the beginning of your prompts and variable content at the end, as caching works on prompt prefixes
3. Calculate potential savings: Measure the token count of your cacheable content and multiply by your query volume to estimate cost reduction
4. Implement gradually: Start with your highest-volume API endpoint, enable caching, and monitor the cost and latency impact before expanding
5. Monitor cache hit rates: Track how often your cache is being utilized to ensure your implementation is effective and adjust cache TTL settings as needed