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

Inference caching is quickly becoming one of the most practical techniques for working with large language models. It lowers request costs, reduces latency, and spares the model from recomputing the same parts of a prompt over and over. For production services with long system instructions and repetitive requests, it is no longer a fine-tuning detail but a basic cost-saving tool.

The core idea: an LLM spends a significant share of its resources not on producing a "smart answer" but on reprocessing context it has already seen. If an application reuses the same system prompt, shared documents, few-shot examples, or common questions, a model without a cache walks that entire path again every time. Inference caching stores the results of these computations and reuses them when the next request matches exactly, or at least comes close enough in meaning.

As a result, the system spends fewer tokens, responds to users faster, and scales more easily under heavy load. At the lowest level sits KV caching. During generation, the model stores internal attention states for each token — key and value pairs — so it doesn't have to recompute them at every subsequent decoding step.

Nearly every modern inference engine does this automatically, and it speeds up a single request. Users usually don't need to enable anything manually, but it's worth understanding that the more visible higher-level optimizations are built on this mechanism. In other words, the KV cache is the foundation that eliminates redundant work within a single model call.

The next layer is prefix caching, which providers also call prompt caching or context caching. The idea is simple: if different requests share the same beginning — say, a long system instruction, a block of rules, a reference document, or a set of examples — that content can be processed once and reused in subsequent calls. But there is a strict condition: the prefix must match byte for byte.

An extra space, a changed punctuation mark, a new date at the start of the prompt, or an unstable key order in JSON can easily kill a cache hit. That's why static content belongs at the beginning, while everything variable — the user's message, the session ID, and the current date — should move to the end. This is also why the technique has already become part of the major players' APIs: Anthropic gives developers explicit control over cached blocks, OpenAI automatically applies prefix caching to long prompts, and Google Gemini offers a separate mechanism for storing context.

In self-hosted environments, vLLM and SGLang support similar logic. The third layer is semantic caching. Here the system stores not intermediate model states but query-response pairs, and it looks for matches by meaning using embeddings and a vector database.

If a user asks almost the same thing as before, the application can return a ready-made answer without calling the LLM at all. This approach is especially useful for FAQs, support bots, and high-volume services where people phrase the same questions in different ways. But the savings come at the cost of extra infrastructure: you need embeddings, vector search, TTLs, and careful tuning of the similarity threshold — otherwise the risk of stale or irrelevant answers goes up.

So semantic caching isn't justified everywhere; it pays off primarily where there is 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 fastest and safest win in production, and semantic caching is worth adding only where question repetition genuinely covers the cost of the extra infrastructure.

For most teams, the optimal path looks like this: first stabilize prompt structure, move all shared context to the beginning, and achieve a high prefix cache hit rate — and only then decide whether a meaning-based cache is needed. For LLM applications, this is a rare case where a single architectural discipline simultaneously cuts costs, speeds up the product, and barely changes the user experience.