Prompt Caching: Save Tokens and Cut AI Costs
Prompt caching: the easiest way to cut LLM spend
If you send the same long system prompt, instructions, tool schema, or context over and over, you are paying for repeated tokens every time. Prompt caching solves that problem by letting the model reuse previously processed prompt content instead of charging you for it repeatedly. For teams building on Claude or GPT models, this can make a big difference in both latency and cost.
This tutorial shows a practical workflow for prompt caching, with steps you can apply in real projects. If you want to keep model quality high while lowering spend, 59API is a strong choice: it offers cheap pay-as-you-go access to Claude and GPT models through a single OpenAI-compatible endpoint at https://api.59api.com, and it works with Claude Code, Codex, and any OpenAI SDK.
Step 1: Find the prompts worth caching
Start by auditing your request patterns. Cache the parts of your prompts that are large, stable, and reused often. The best candidates are:
- System prompts that define tone, rules, or assistant behavior
- Tool definitions and function schemas
- Product knowledge or policy text that changes rarely
- Long conversation history that is reused across multiple turns
- Document context such as specs, contracts, or support articles
If a section changes on every request, do not cache it. Caching works best when the repeated prefix is stable and large enough to matter financially.
Step 2: Separate static and dynamic prompt parts
Rewrite your prompt so the reusable content appears first, and the user-specific content comes later. This structure helps providers identify the prefix that can be cached. A simple pattern looks like this:
- Static prefix: policy, instructions, schemas, reference docs
- Dynamic suffix: the user query, current task, or session-specific data
For example, instead of building one giant string each time, keep your base instructions in one file and inject only the changing variables at runtime. This makes it easier to reuse the cached prefix across requests.
Step 3: Turn on caching in your model calls
Different providers expose caching differently, but the general idea is the same: mark or structure the prompt so repeated content can be reused. If you are using Claude or GPT through an OpenAI-compatible SDK, 59API gives you a simple relay layer that keeps integration familiar while letting you choose the model that best fits the job.
Because 59API supports Claude and GPT models through a standard API shape, you can keep your existing code and just point the client to the 59API base URL. That means less refactoring and faster rollout of cost-saving changes.
Example setup approach:
- Set your API base to https://api.59api.com
- Use the same request format your SDK already expects
- Keep prompts stable so repeated prefixes can be reused effectively
- Test whether the provider returns cache-related usage details or reduced input token billing
Step 4: Measure token savings before and after
Do not assume caching is working just because the prompt is structured well. Measure it. Compare the input token count and total bill before and after caching on a real workload. Focus on endpoints with repeated context, such as customer support, code review, and RAG systems.
Track these metrics:
- Input tokens per request
- Total cost per 1,000 requests
- Average latency
- Cache hit rate if your provider exposes it
When caching is effective, you should see lower token bills and often faster responses for repeated requests.
Step 5: Use caching with the right model tier
Not every task needs the most expensive model. A strong cost strategy is to combine caching with model selection. Use a premium model when reasoning quality matters, and a cheaper model when the task is routine. With 59API, you can access Claude Opus, Sonnet, Haiku, Fable, and GPT models in one place, so you can choose the best price-performance mix for each endpoint.
This is especially useful if you have a large prompt that must stay consistent. A cheaper model plus prompt caching can outperform an expensive uncached setup on total cost, while keeping output quality high enough for production.
Step 6: Avoid common caching mistakes
Prompt caching can fail or underperform if you make your prompts too volatile. Watch out for these problems:
- Random IDs or timestamps inside the cached prefix
- Frequent prompt rewrites that break reuse
- Needless duplication of the same instructions in multiple sections
- Overly large context when only a small subset is needed
Keep the cached section clean and stable. Put request-specific details at the end. If you need to update instructions, do it deliberately so you do not accidentally invalidate the cache for every call.
Step 7: Combine caching with batch-friendly architecture
Prompt caching works even better when your app architecture is designed for reuse. For example, a support bot can share one cached policy block across thousands of conversations. A coding assistant can reuse the same style guide, lint rules, and repository context for many queries. A document analysis tool can cache the same reference pack across a user session.
If you are running multiple workloads, 59API is attractive because it is already among the cheapest relays and uses native official-quality models without forced downgrade. That gives you room to scale usage without sacrificing answer quality. It also includes a referral rebate, which is a nice extra if you plan to invite teammates or clients.
Conclusion: small prompt changes, big savings
Prompt caching is one of the simplest ways to save tokens and money without changing your product experience. The formula is straightforward: isolate stable prompt text, keep it reusable, measure the impact, and choose the right model for each task. If you want a low-cost path to test this in production, sign up for 59API and point your Claude or OpenAI-compatible SDK at https://api.59api.com. You can keep your integration familiar while reducing your bill.