Caching Strategies for LLM Applications

Every token that goes through a large language model costs money and adds latency. When you build an application that serves many users — or a single user with many repeated requests — a meaningful chunk of those tokens are redundant. The same system prompt, the same retrieved documents, the same questions phrased slightly different ways. Caching is the practice of recognizing that redundancy and skipping work you've already done.

For LLM apps, "caching" isn't one technique. Three distinct approaches solve different problems: prompt caching (provider-managed, for repeated prefixes), semantic caching (similarity-based, for near-duplicate queries), and result caching (application-level, for deterministic outputs). Most production systems end up using more than one, layered together. Each has its own tradeoffs around correctness, cost, and complexity.

Prompt caching: skip work on long, repeated prefixes

Prompt caching is a provider-side feature where the model API recognizes a shared prefix across requests and charges you less — in both money and latency — for the cached portion. Anthropic's Claude and OpenAI's GPT models both support variants of this. You send the same system prompt, retrieved context, or few-shot examples in every call, and the provider hashes the prefix and reuses the internal computation (the key-value cache inside the transformer) instead of reprocessing it.

The savings can be substantial for long prompts. A large system prompt with retrieved documents might take noticeable time to process fresh; with a cache hit it's nearly free and almost instant. The catch is that cache entries have a TTL (time-to-live) — typically minutes to a few hours — and they're keyed on exact prefix matching. Change one character in your system prompt and the cache is gone. Providers also charge a small fee to write to the cache, so it only pays off if you expect to reuse the prefix across multiple requests within the TTL window. It's well suited to RAG pipelines, agentic loops with stable instructions, and any app where the bulk of the prompt is fixed context followed by a short, varying user query.

Semantic caching: match on meaning, not exact text

Semantic caching goes one step further. Instead of requiring an exact prefix match, you embed each incoming query into a vector (a numeric representation of its meaning), search a store of previously answered queries for ones that are semantically close, and return the stored answer if the similarity passes a threshold you set. A user asking "how do I reset my password?" and "what's the process to reset my login?" would hit the same cache entry.

This is useful in conversational or support-style apps where users phrase the same intent many ways, and where approximate correctness is acceptable. Tools like GPTCache and vector-enabled Redis make this straightforward to set up. The tradeoff is real, though: embeddings measure surface similarity, not factual equivalence. "What's the capital of France?" and "What's the capital of Germany?" are structurally identical and will have high similarity — but very different answers. Setting your threshold too low produces wrong answers; too high and you rarely hit the cache at all. Semantic caching also assumes the underlying answer hasn't changed since you cached it, which breaks for anything time-sensitive. Most teams who use it successfully put it behind careful evaluation and treat it as a latency optimization for well-understood query patterns, not a general-purpose cost saver.

Result caching: cache deterministic outputs by input hash

Result caching is the most boring and the most reliable. You hash the exact inputs to your LLM call — model name, system prompt, user prompt, temperature, and any other parameters that affect output — and store the model's response in a key-value store. The next time you see the same inputs, you return the stored response without calling the model at all. Redis, Memcached, or even a local in-memory map will do.

This works best for tasks where the output is deterministic or near-deterministic: structured extraction with `temperature=0`, classification, tool use, code generation for boilerplate. It doesn't work for creative or open-ended generation, and it doesn't help when the prompt depends on external state like a database query or current date unless you include that state in the cache key. The main design question is cache invalidation — how long a stored result stays valid. For pure functions of fixed inputs, it's effectively forever. For anything that might drift (model version changes, retrieved docs updated), you need a TTL or an explicit invalidation step. Result caching pairs well with prompt caching: prompt caching saves on the expensive part of repeated-prefix calls, while result caching skips the call entirely when the inputs line up exactly.

Choosing what to layer

There's no single right answer, but a rough pattern holds across production LLM systems. Start with result caching for any deterministic, high-frequency call — it's cheap to add and has clear semantics. Add prompt caching when your prompts are long and repeated — it's effectively free if your provider supports it, since it's just a parameter on your API call. Semantic caching is the riskiest of the three because of its potential for silently returning the wrong answer, so add it last, for narrow use cases where you can measure the hit rate and false-positive rate, and where approximate answers are acceptable.

If you're building LLM features for production, the jobs feed and tools on Neural Goldmine list roles where this kind of infra thinking is exactly what teams are hiring for. Caching decisions are rarely visible to users — but they're the kind of detail that separates a demo that loses money from a feature that survives real traffic.