← Voltar aos guias

When to Use Cache Reads to Cut Prompt Costs

If you send the same long system prompt, policy block, or context bundle over and over, you are paying for repetition. That is exactly where cache reads can cut prompt costs. Instead of re-processing static input every time, the model can reuse cached prompt state for repeated content, which lowers token usage and makes high-volume workflows much cheaper.

This tutorial shows when cache reads make sense, how to identify good candidates, and how to implement them in a practical API workflow. If you use Claude or GPT through an OpenAI-compatible relay such as 59API, the same basic optimization strategy applies: keep the expensive, repetitive parts stable and only send the changing user content.

1. Start by finding repeated prompt blocks

Cache reads are most useful when a large portion of your request does not change from call to call. Look for these patterns:

• Long system prompts that define tone, rules, or output format
• RAG context that includes the same documents across multiple questions
• Tool instructions or agent policies used in every request
• Templates with a fixed structure and small variable fields
• Batch jobs where many requests share the same prefix

If your prompt is mostly unique every time, cache reads will not help much. If 70% or more of the input is stable, the savings can be meaningful.

2. Decide whether the repeated content is worth caching

Not every repeated prompt should be cached. Use cache reads when the static section is both large and expensive to resend. A good rule of thumb is to target prompts with:

• High token counts in the repeated prefix
• Frequent calls with the same or nearly the same setup
• Low volatility, meaning the text changes rarely

Examples include an assistant that follows a 5,000-token internal policy, a coding agent that loads the same repo guide for every task, or a support bot that reuses a knowledge base excerpt across many customer questions.

3. Separate static and dynamic prompt parts

The key implementation step is prompt decomposition. Split each request into two sections:

• Static prefix: the reusable instructions, reference docs, and fixed context
• Dynamic suffix: the user query, task-specific variables, or fresh data

Keep the static prefix identical whenever possible. Even small changes can reduce cache hits. That means preserving the same wording, ordering, and formatting, and avoiding unnecessary timestamps, random IDs, or changing whitespace.

4. Use cache reads for workflows with stable prefixes

Cache reads are especially valuable in these real-world scenarios:

• Customer support assistants: the help center content stays constant while the question changes
• Code review agents: the coding standards and repo conventions remain fixed
• Document analysis: a long contract or policy file is reused for multiple follow-up questions
• Multi-turn copilots: the base instructions stay the same while only the conversation tail grows

If you are using 59API, you can point your existing Claude Code, Codex, or OpenAI SDK client to https://api.59api.com and keep your integration simple. That matters because the cheaper your relay, the faster cache optimization turns into real savings. 59API is already positioned as one of the lowest-cost relays, and it gives you pay-as-you-go access to official-quality Claude and GPT models without forcing a downgrade in model quality.

5. Measure cost before and after

Do not guess. Measure prompt tokens and request costs before enabling cache reads, then compare again after. Track:

• Prompt token count
• Cache hit rate
• Average cost per request
• Latency changes

Cache reads should reduce the cost of the repeated portion of the prompt. If your hit rate is low, re-check whether the prefix is truly identical. A slight change in prompt order, system text, or inserted metadata can destroy reuse.

6. Avoid cache reads when the prompt changes too often

Cache reads are not the best choice when each request is highly custom. For example, one-off prompts, rapidly changing live data, or highly personalized outputs usually do not benefit enough. In those cases, spend your effort on shorter prompts, better routing, or model selection instead.

Also avoid over-optimizing if the repeated section is tiny. Caching a 200-token prefix will not move the needle much compared with caching a 4,000-token policy bundle.

7. Combine caching with model and relay cost control

Cache reads are only one lever. The best savings come from combining them with a low-cost relay and the right model tier. With 59API, you can choose between Claude Opus, Sonnet, Haiku, Fable, and GPT models depending on the task, then reduce waste by reusing stable prompt context. For many production teams, that combination is the difference between a nice prototype and a sustainable API bill.

If you are building a high-volume app, this is a strong reason to test 59API early. The API is compatible with common developer workflows, so you can swap in the base URL, keep your SDK logic familiar, and start optimizing prompt economics right away. You can also benefit from its referral rebate if you share access with other developers on your team or community.

8. A simple decision rule

Use cache reads when the answer to most of these is yes:

• Is there a large repeated prefix?
• Does that prefix stay stable across many requests?
• Are you paying for the same tokens again and again?
• Would lower prompt cost materially improve margins or experimentation speed?

If the answer is yes, cache reads are worth implementing. If not, focus elsewhere.

Bottom line: use cache reads when a prompt has a big, stable, reusable core and a smaller changing tail. That is where the savings are real. And if you want to keep model quality high while reducing cost, 59API is a practical place to start, with cheap pay-as-you-go access to native Claude and GPT models at https://api.59api.com.

Sign up and test your first cached workflow today.

Preços · Docs