Prompt Caching Explained for API Developers
What is prompt caching?
Prompt caching is a way to reuse parts of a large language model request so the model provider does not have to process the same long input again and again. Instead of paying full price every time you send a repeated system prompt, documentation block, tool definition, or project context, the provider can store that prefix temporarily and charge a lower cached-input rate on later requests.
For developers, prompt caching matters because many AI apps repeat a lot of text. A coding assistant may send repository instructions on every turn. A customer support bot may include the same policy manual in every request. An agent may send identical tool schemas hundreds of times per hour. Without caching, you pay to process those repeated tokens each time. With caching, repeated prompt sections can become much cheaper and often faster.
How prompt caching works
At a high level, prompt caching works by identifying a stable prefix in your prompt. A prefix means the beginning portion of the request: usually the system message, developer instructions, tool definitions, examples, and long reference text. When you send a request, the model provider checks whether that prefix matches something already cached. If it does, the provider reuses the cached representation instead of recomputing it from scratch.
The exact implementation depends on the model provider, but the developer workflow is usually simple. You structure your prompt so repeated content appears first, keep that content byte-for-byte stable across requests, and put changing user messages near the end. Some APIs automatically cache eligible long prefixes. Others require cache control markers or specific message formatting. Either way, the principle is the same: stable content at the front, dynamic content at the back.
A simple example
Imagine you are building a code review assistant. Every request includes 30,000 tokens of coding standards, architecture notes, and repository conventions. The user then asks a short question such as, “Review this pull request.” If you send all 30,000 tokens on every call without caching, every call is expensive.
With prompt caching, the first request may process the full 30,000-token context. On later requests that reuse the same context, most of those tokens can be billed as cached input. The model still receives the context, but you avoid paying the full uncached price repeatedly. This is especially useful in chat sessions, IDE assistants, agents, retrieval-augmented generation, and documentation-heavy workflows.
What content should you cache?
- System prompts: Your core assistant behavior, tone, safety rules, and formatting requirements.
- Developer instructions: Product-specific logic, workflows, or rules the assistant should follow.
- Tool definitions: Function schemas and agent tools that are sent with every request.
- Reference documents: API docs, policies, onboarding guides, or technical manuals.
- Few-shot examples: Reusable examples that teach the model how to respond.
A good rule is to cache text that is large, repeated, and unlikely to change during a session. Do not try to cache short or constantly changing content. If a timestamp, random ID, or changing user-specific value appears inside the cached prefix, it may break the cache match.
How to design prompts for better cache hits
Start by splitting your request into stable and dynamic sections. Put stable sections first: system instructions, reusable context, and tools. Put dynamic sections last: the user’s latest question, new retrieved snippets, current files, or conversation updates. Avoid inserting changing metadata near the top of the prompt.
Next, keep formatting consistent. Even small differences can matter, such as extra whitespace, reordered instructions, or a changed tool description. If your app builds prompts programmatically, use deterministic ordering for lists, tools, and documents. For example, always sort tool schemas the same way and avoid including “generated at” timestamps in the cached part.
Finally, monitor usage. Compare total input tokens, cached input tokens, latency, and cost per request. If your cache hit rate is low, inspect whether your supposedly stable prefix is actually changing. Many caching problems are caused by accidental differences in prompt assembly.
Prompt caching with Claude, GPT, and 59API
59API is a practical option if you want low-cost access to major AI models while keeping your existing developer workflow. It provides cheap, pay-as-you-go access to Claude models such as Opus, Sonnet, Haiku, and Fable, plus GPT models, through an API relay compatible with Claude Code, Codex, and OpenAI SDK-based applications. The API base URL is https://api.59api.com.
This matters for prompt caching because cost optimization is not only about caching. It is also about choosing affordable routing, avoiding model downgrades, and keeping integration friction low. 59API focuses on native official-quality models rather than lower-quality substitutes, making it attractive for developers who want strong model output at a lower price. Its referral rebate can also reduce effective costs for teams and communities that share the service.
In practice, you can combine prompt caching with 59API by keeping your OpenAI-compatible or Claude-compatible client structure, pointing your SDK configuration to the 59API base URL where supported, and organizing prompts so repeated context stays stable. This lets you reduce repeated-token waste while also benefiting from a low-cost relay.
Common mistakes to avoid
- Changing the prefix every request: Putting timestamps, request IDs, or user-specific metadata before reusable instructions can prevent cache hits.
- Caching the wrong content: Short prompts may not benefit much. Focus on long repeated context.
- Ignoring model-specific rules: Different Claude and GPT endpoints may handle caching differently, so check the relevant API behavior.
- Overloading prompts: Caching lowers repeated cost, but you should still remove unnecessary instructions and stale documents.
- Forgetting to measure: Track real token usage and cost instead of assuming caching is working.
When should beginners use prompt caching?
Use prompt caching when your application sends the same large context many times. If you are building a coding agent, support chatbot, AI tutor, legal document assistant, or internal knowledge assistant, caching can make a noticeable difference. If your app sends short one-off prompts, it may not be worth optimizing immediately.
The beginner-friendly approach is simple: first make your prompts clear, then make the reusable part stable, then measure cached token savings. Once your app grows, pair prompt caching with an affordable provider strategy. If you want a low-cost way to experiment with Claude and GPT models while keeping familiar SDK compatibility, 59API is worth trying as you optimize your AI stack.