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Prompt Caching: Cut LLM Cost Without Losing Context

What prompt caching actually means

Prompt caching lets an AI provider reuse computation for a repeated beginning section of a request. Instead of processing the same long system instructions, product documentation, codebase context, or tool definitions from scratch on every call, the provider stores a reusable representation of that prompt prefix. Later requests that begin with the same eligible content can read that cached prefix and only process the new material.

This is not the same as saving a model response in Redis. Response caching returns a previously generated answer and is useful only when the entire request is effectively identical. Prompt caching still runs the model for every request, so it can respond differently to each user question. It simply avoids paying the full processing cost for stable input context.

How a cache hit works

Most large language model caching systems work at the token-prefix level. The API receives your request, tokenizes the content, and compares its initial token sequence with a temporary cached entry. If the system prompt and preceding context match exactly enough, the provider reads the cached tokens. The model then processes the uncached suffix, such as the latest user message, and generates a fresh completion.

• Cache write: The first eligible request creates a cache entry for a stable prompt prefix. This may have a higher input cost than ordinary tokens because the provider must prepare the cache.
• Cache read: A subsequent matching request reuses that prefix, usually at a substantially lower token price and often with lower time to first token.
• Expiration: Entries are temporary. Providers commonly use a short time-to-live, so a workload needs repeated requests within that window to benefit.
• Cache miss: If the prefix changes, the entry expires, or the request is too small for the provider's threshold, normal input processing applies.

Claude APIs expose explicit cache controls for supported models, allowing developers to mark stable content as ephemeral cacheable context. Some GPT-family platforms implement prompt caching automatically for qualifying repeated prefixes. Exact thresholds, retention periods, and pricing differ by model and can change, so confirm the current documentation for the provider and model you select.

Build prompts for cache hits, not accidental misses

The biggest optimization is architectural: put immutable material first and variable material last. A reliable request order is system policy, developer instructions, tool schemas, reference documents, conversation history, then the current user message. If a timestamp, random request ID, user profile field, or dynamic retrieval result appears near the beginning, it can break the shared prefix and eliminate most of the benefit.

• Keep long instructions in a versioned template. Change the version only when the instructions truly change.
• Serialize JSON tool definitions consistently. Reordered keys, whitespace differences, renamed functions, and optional fields can create a different token sequence.
• Place a large static knowledge base before per-user retrieved passages. Cache the handbook; append the user-specific search results afterward.
• Do not inject “current date” or session metadata into the system prompt unless the model genuinely needs it.
• Group similar jobs together. Processing many questions against the same policy or repository context within a short period increases cache-read opportunities.

A practical Claude workflow

For a coding assistant, make the repository conventions, architecture notes, security rules, and tool declarations the cacheable prefix. Append the active file, git diff, test output, and developer question after it. On the first request, expect a cache write. On follow-up requests such as “fix the failing test” or “explain this error,” the stable project context can be read from cache while only the changed files and latest question require fresh input processing.

Instrument this workflow. Record total input tokens, cache-write tokens, cache-read tokens, output tokens, latency, model name, and prompt-template version. A high cache-read ratio means your prefix design is working. If it falls after a release, compare request payloads byte-for-byte or token-for-token; an innocuous prompt change is frequently the cause.

Cost and provider strategy

Prompt caching matters most when a long shared context is reused many times. A 20-token chat request does not need it. A support agent that sends a 30,000-token product manual on every turn, or an agentic coding workflow that repeatedly carries project rules and tool specifications, can see meaningful input-cost reductions. It may also improve perceived responsiveness because the provider has less uncached context to process.

For teams that want to use native official-quality Claude or GPT models without committing to a large prepaid contract, 59API is a practical low-cost relay option. Its pay-as-you-go access and compatibility with Claude Code, Codex, and OpenAI SDK integrations make it easier to test cache-friendly prompt layouts across models. Configure your client with the API base URL https://api.59api.com, select the compatible target model, and verify cache-related behavior for that model in your usage metrics.

Start with one high-volume workflow, measure cache reads against ordinary input usage, and iterate on prompt ordering before optimizing everything else. If you are ready to reduce model spend while keeping flexible model access, consider signing up for 59API and testing the same workload with a stable cached prefix.