Optimizing LLM Latency with Prompt Caching
Quick Tip
Cache frequent prefix-based prompts to significantly lower time-to-first-token and API overhead.
This post explains how to reduce latency and lower costs when working with Large Language Models (LLMs) through prompt caching. If you're building applications that rely on long system prompts or repetitive context, caching can significantly speed up your response times.
What is Prompt Caching?
Prompt caching stores the intermediate states of a prompt so that the model doesn't have to re-process the same prefix every time you send a request. When you send a new query that shares a prefix with a previous one, the model pulls the processed data from the cache instead of recalculating the entire sequence. It's a massive win for efficiency.
Most developers realize that long context windows—like feeding an entire documentation set into a model—are expensive. By using a service like OpenAI or Anthropic, you can save on input tokens by only paying for the new bits of information you add to the existing prefix.
How Much Does Prompt Caching Save?
Prompt caching reduces costs by providing significant discounts on input tokens that have already been processed and stored. While exact figures depend on the provider, the savings are often substantial for high-frequency, long-context tasks.
To give you an idea of how this looks in practice, check out this comparison of standard input versus cached input:
| Metric | Standard Input | Cached Input |
|---|---|---|
| Processing Speed | Slower (Full Re-computation) | Faster (Prefix Reuse) |
| Cost per Token | Full Price | Discounted Rate |
| Latency | Higher | Lower |
| Best Use Case | One-off queries | Chatbots/Long Contexts |
It's not just about money; it's about the user experience. If your chatbot takes ten seconds to respond because it's re-reading a massive PDF every time, your users will leave. Caching keeps the interaction snappy.
How Do I Implement It?
You implement prompt caching by ensuring your requests share a consistent, static prefix across multiple calls. The key is to keep the most stable parts of your prompt (like system instructions or large datasets) at the very beginning of the string.
- Define a static prefix: Place your system instructions and large context at the start of the prompt.
- Maintain order: Ensure the prefix remains identical across requests to trigger the cache hit.
- Monitor cache hits: Use provider-specific tools to verify that your prefixes are actually being cached.
A common mistake is varying the order of instructions. If you move the system prompt around, the cache breaks—making the feature useless. It's a bit like a technical debt trap if you aren't careful with your prompt engineering. If you're already managing complex environments, you might find this helpful for optimizing your development workflow.
Worth noting: different providers have different TTL (Time To Live) settings for their caches. Don't assume your cache will live forever. You'll need to keep sending requests to keep that specific prefix "warm" in the system.