OpenAI, Anthropic, and Gemini: How Inference Caching Reduces LLM Cost and Latency

Inference caching is rapidly becoming one of the most practical techniques in working with large language models: it reduces request costs, decreases latency, and eliminates the need to recalculate the same parts of a prompt repeatedly. For production services with long system instructions and recurring requests, this is no longer a subtle optimization but a fundamental cost-saving tool. The core idea is that an LLM spends a significant portion of its resources not on generating a "smart answer," but on redundant processing of already-familiar context.

If an application has the same system prompt, shared documents, few-shot examples, or standard questions, the model without caching traverses this path anew each time. Inference caching preserves the results of such computations and reuses them when the next request matches completely or is sufficiently similar in meaning. As a result, the system consumes fewer tokens, responds to users faster, and scales more easily under high load.

At the basic level, KV-caching operates. During generation, the model preserves internal attention states—key-value pairs—token by token to avoid recalculating them at each subsequent decoding step. This happens automatically in nearly all modern inference engines and accelerates a single specific request.

Users typically don't need to enable anything manually, but it's important to understand: this mechanism forms the foundation for more significant higher-level optimizations. In other words, KV-cache is the foundation that eliminates redundant work within a single model invocation. The next layer is prefix caching, which providers also call prompt caching or context caching.

The idea is straightforward: if different requests share the same beginning—such as a long system instruction, a block of rules, a reference document, or a set of examples—they can be processed once and reused in subsequent calls. But there's a strict condition: the prefix must match byte-for-byte. An extra space, changed punctuation, a new date at the start of a prompt, or an unstable key order in JSON easily kills a cache hit.

Therefore, it's better to place static content at the beginning and move all variables—the user's message, session ID, and current date—to the end. This is precisely why this technique has already become part of the API of major players: Anthropic gives developers explicit control over cacheable blocks, OpenAI automatically applies prefix caching for long prompts, and Google Gemini offers a separate context storage mechanism. In self-hosted environments, similar logic is supported by vLLM and SGLang.

The third layer is semantic caching. In this case, the system stores not intermediate model states, but query-response pairs and searches for semantic matches through embeddings and a vector database. If a user asks nearly the same thing as before, the application can return a ready-made answer without calling the LLM at all.

Such an approach is especially useful for FAQs, support bots, and mass-market services where people ask the same questions in different words. But this savings comes at the cost of additional infrastructure: you need embeddings, vector search, TTL, and careful threshold tuning; otherwise, there's a risk of stale or irrelevant answers. Therefore, semantic caching is justified not everywhere, but primarily where there's a large stream of similar requests and a high chance of reusing an already-generated answer.

What does this mean in practice? KV-caching already works on its own, prefix caching usually delivers the quickest and safest win in production, and semantic caching should only be added where question repetitiveness truly covers the cost of additional infrastructure. For most teams, the optimal path looks like this: first, stabilize prompt structure, move all shared context to the beginning and achieve high cache hit rates for prefixes, and then decide whether semantic caching is needed.

For LLM applications, this is a rare case where one architectural discipline simultaneously cuts costs, accelerates the product, and almost doesn't change the user experience.