Skip to main content

Stop Wasting Tokens: The Deep-Dive Guide to Prompt Caching

· 5 min read

If you’ve used terminal-based AI agents like Claude Code, Cursor, or custom multi-turn coding assistants, you’ve probably noticed something remarkable: they read entire codebases, file trees, git diffs, and project specs, yet consistently return fast responses across dozens of turns.

How do these tools feed tens of thousands of context tokens into a model on every single turn without causing massive latency spikes or huge API bills?

The underlying engine is prompt caching. Whether you’re building agentic coding workflows or optimizing AI APIs in production, prompt caching is a fundamental architectural pattern. It can cut API costs by up to 90% and slash Time-to-First-Token (TTFT) latency by 80%.

What Is Prompt Caching?​

Prompt caching operates differently from traditional application-level caching (such as saving output strings in Redis). In standard web caching, a slightly modified user query results in a cache miss and requires processing the full request from scratch. Prompt caching, by contrast, operates at the model architecture level.

During the compute-heavy prefill phase, a large language model processes input by calculating mathematical representations of attention for every token. These representations are Key-Value (KV) tensors.

Prompt caching stores those KV tensors for static prefixes directly in GPU cluster memory. When an application sends a new request sharing the exact same starting text (such as a project file tree, system prompt, or git context) the model skips re-tokenizing and re-processing that prefix. It pulls the pre-computed KV pairs from memory and immediately begins generating the response, preserving dynamic context without the usual overhead.

Why Prompt Caching Matters​

Implementing prompt caching addresses three main bottlenecks in LLM integration:

  1. Cost Reduction: Input tokens drive API costs. Because cached tokens bypass full matrix multiplication during the prefill phase, API providers offer steep discounts (typically 50% to 90%). In an agent loop making 30 sequential calls over a large repository, caching can reduce the cost of a run from several dollars down to cents.
  2. Sub-Second Latency: Prefill delay is the primary bottleneck when passing large context windows. Caching eliminates this processing delay, yielding lower Time-to-First-Token performance.
  3. Feasibility for Repository-Scale Workflows: Multi-turn coding tools rely on persistent context: tool definitions, linting rules, open file contents, and history. Without caching, running context-heavy developer tools at scale becomes cost-prohibitive and impractical.

How Prompt Caching Works Under the Hood​

The execution flow of prompt caching across multi-turn developer sessions follows three key steps:

  • Token Hashing & Breakpoints: The provider hashes the prompt starting from token zero (0). Tokens are evaluated in structured blocks (e.g., 128-token increments once hitting a minimum threshold like 1,024 tokens). Providers such as Anthropic also support explicit cache control markers, allowing developers to specify precisely where static context ends.

  • Cache Lookup: The API gateway checks GPU memory for a matching hash.

  • Cache Hit: An exact prefix match within the Time-To-Live (TTL) window causes the model to load KV tensors directly from memory.

  • Cache Miss: If a character in the prefix changes, the model computes the prompt from scratch and writes new KV tensors to the cache.

  • Streamlined Inference: The model isolates the new tokens (such as a recent terminal command or file edit) and runs live inference exclusively on that incoming delta.

Best Practices for High Cache Hit Rates​

To achieve optimal cache hit rates in custom applications, structure prompts according to three primary layout principles:

  • Top-Heavy Prompt Structure: Place all static content at the start of the prompt payload. System instructions, tool schemas, repository maps, and reference documentation belong at the top. Dynamic elements (user messages, timestamps, or tool execution outputs) must go at the end.
  • Maintain Contiguous Matching: Prefix matching is deterministic from token zero. Changing a single character, reordering imported files, or inserting dynamic timestamps near the beginning of a prompt invalidates the downstream cache.
  • Utilize Ephemeral Retention: Cache TTL windows automatically refresh upon hit. Interactive developer sessions naturally maintain active caches throughout extended development workflows as long as the prefix remains stable.

Implementation & Next Steps​

Designing prompts around prefix stability allows high-context, low-latency agentic workflows to run efficiently at scale.

If you are currently implementing prompt caching in your application, specify your API provider (Anthropic, OpenAI, Gemini, or DeepSeek) to determine the exact header configurations, cache boundaries, and parameter structures required for your stack.

Have a question or suggestion?

There is more than one way to start a conversation: