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Prompt Caching: A 15-Minute API Cost-Cutting Guide

What prompt caching actually saves

If your application sends the same instructions, tool definitions, or reference material repeatedly, you may be paying full input rates for reusable work. Prompt caching lets a supported provider reuse previously processed prompt content, potentially reducing input charges and time to first token.

Caching does not shorten your prompt or make output tokens free. Cached content still occupies context. The benefit comes from discounted cached-input processing, subject to the provider’s pricing, minimum cacheable length, expiration rules, and routing behavior.

Here is a quick-start workflow for getting measurable savings without redesigning your application.

1. Check support before changing your code

Confirm that your selected model, API endpoint, and relay route support prompt caching. Some APIs cache eligible prefixes automatically; others require explicit cache controls or a separately created cached resource. Do not assume an OpenAI-compatible interface makes every provider’s caching behavior identical.

With 59API, start from the service base URL, https://api.59api.com, and use the documented compatibility endpoint for your client. Copy the exact model identifier from the dashboard rather than guessing it from a display name.

59API offers low-cost, pay-as-you-go access to native, official-quality models without downgrades, including Claude Haiku 4.5 and GPT-6.1 Sol. It works with OpenAI SDKs and developer tools including Claude Code, Codex, and Gemini CLI. Before relying on cache savings, verify cache support, billing treatment, and usage reporting for your chosen route.

2. Put stable content first

For prefix-based caching, organize requests so repeated content forms an identical beginning. A changing timestamp near the top can prevent the later, otherwise identical material from matching.

• First: stable system instructions, response rules, and reusable examples.
• Next: unchanged tool schemas and shared reference documents.
• Last: the current question, retrieved snippets, timestamps, and request-specific identifiers.

A support assistant, for example, can send its fixed policy manual before the customer’s latest question. Keep document ordering and serialization deterministic. Avoid unnecessary whitespace changes, rotating examples, or randomly reordered JSON keys in reusable content.

For growing conversations, preserve earlier messages exactly and append new turns. Previously shared prefixes may remain reusable, although expiration and cache eviction still apply.

3. Choose the right caching mechanism

Automatic caching: send eligible requests normally, preserving their shared prefix. There may be no additional request field to set. Check the documented minimum prefix length; a tiny instruction block may not qualify.

Explicit caching: follow the provider’s instructions for cache boundaries, cache-control fields, or cached-resource references. Check whether creating or retaining a cache incurs extra charges. Use provider-specific request options only when your SDK and relay explicitly support passing them through.

Keep repeated traffic on the same model and compatible route when testing. Do not assume caches are shared across providers, accounts, model versions, or routing destinations.

4. Verify a hit instead of guessing

• Send one request containing a sufficiently long, stable prefix.
• Repeat it promptly, changing only the final user question.
• Inspect raw usage metadata for cached-input or cache-read token counts. Field names vary by API.
• Compare billed cost and time to first token across several repetitions.
• Change an early prefix detail and observe whether reuse decreases.

Log model, route, input tokens, cached tokens, output tokens, latency, and billed amount. Lower latency alone is not proof of a cache hit. If cache metadata is missing, ask the relay how to verify cache billing before estimating savings.

5. Calculate savings using your traffic

Suppose 100 requests each contain a 10,000-token shared prefix and a 1,000-token unique suffix. Without caching, that is 1.1 million full-price input tokens.

Assume the first prefix is uncached, all subsequent prefixes hit, regular input costs $1 per million tokens, and cached reads cost $0.10 per million. Input spending becomes $0.11 for uncached tokens plus $0.099 for cached tokens: $0.209 instead of $1.10, about 81% less.

These are illustrative rates, not 59API prices. Include cache-write premiums, storage charges, misses, and output costs in your actual calculation.

Start with one repetitive workload

Pick a documentation assistant or coding workflow with substantial repeated context. Measure its baseline, stabilize the prefix, then compare actual invoices. Sign up for 59API if you want a low-cost starting point for testing across supported models; its referral rebate is an additional benefit, separate from caching savings.

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