Prefix Caching

What Is Prefix Caching?

Prefix caching (also known as prompt caching) is an optimization technique for LLM inference that saves time and money by reusing work the model has already performed. When you send a prompt to an AI model, it performs a heavy computational process to understand the relationships between every word. If you send the same starting text—the “prefix”—multiple times, prefix caching allows the model to skip re-calculating that portion, jumping straight to generating new information.

Think of it like reading a long legal contract. If you have to read the same 50-page document every day to answer one new question, you would eventually memorize the document so you could focus only on the new question. Prefix caching gives the AI that same ability to “remember” the static parts of your input.

How It Works: The KV Cache Reuse Mechanism

To understand how this works, we look at the prefill phase. This is the initial, compute-intensive step where the model reads your entire input prompt at once. During this phase, the model breaks text into tokens (the basic units of text) and computes attention patterns—mathematical representations of how words relate to one another. These patterns are stored in what is called a KV cache (Key-Value cache).

Normally, every time you send a request, the model builds this cache from scratch. With prefix caching, the system checks if the beginning of your prompt matches a previously stored KV cache. If it finds a match, it simply loads that existing data into memory. By skipping the recomputation of these shared prefixes, the model significantly reduces the time it takes to start generating an answer.

Provider-Managed vs. Self-Hosted Implementations

Depending on how you access your models, prefix caching is handled in different ways:

• Provider-Managed: Major AI providers have built this directly into their APIs. For example, Anthropic offers prompt caching on Claude models, which can lead to significant cost and latency reductions. Other providers, such as OpenAI and Google, offer various forms of automatic caching or discounts for repeated input segments.
• Self-Hosted: If you are running your own infrastructure, you have to enable these features manually. Tools like vLLM implement Automatic Prefix Caching using block-hash-based reuse, which you can activate with a configuration flag. Others, like SGLang, use a method called RadixAttention, which organizes the cache in a tree structure to efficiently match prefixes.

Why Prefix Caching Matters for AI Agents

Prefix caching is especially valuable for AI agents. Unlike a simple chatbot, an agentic workflow often involves complex, repetitive instructions. These agents frequently rely on large system prompts, extensive tool definitions, and long background documents that remain constant across hundreds of individual requests.

Because these shared prefixes are so substantial, caching them allows agents to operate much faster and more affordably. Instead of the model “re-reading” the entire rulebook and toolset for every single step of a task, it keeps that information ready to go, allowing the agent to focus its compute power on the actual problem-solving.

Best Practices for Maximizing Cache Hits

To get the most out of prefix caching, you need to structure your prompts strategically:

• Front-load static content: Place your system prompts, tool definitions, and reference documents at the very beginning of your prompt.
• Keep dynamic content at the end: Anything that changes with every request—like a user’s specific question or a unique timestamp—should be placed after the static prefix.
• Avoid “noise” in the prefix: Do not insert timestamps, unique request IDs, or random UUIDs into the beginning of your prompt. Even a single changed character will break the cache match, forcing the model to recompute everything.
• Mind the threshold: Many providers require a minimum prefix length (often around 1,000 tokens) before caching is triggered.

Common Questions

How is this different from other inference optimizations? Prefix caching specifically reuses computation for identical prefix segments. Other techniques target different bottlenecks: some compress the input before inference, while others route requests to different-sized models based on complexity. Prefix caching is unique in that it eliminates redundant work rather than reducing or redirecting it.

What is the future of this technology? The research frontier is moving toward Learned Prefix Caching (LPC). Instead of relying on simple rules to decide what to evict from the cache, LPC uses machine learning to predict which parts of a prompt are likely to be reused, which can significantly reduce the required cache size compared to standard methods.