# Prompt Caching vs Semantic Caching for LLM APIs: When to Use Each

> Understand the difference between prompt caching and semantic caching for LLM APIs, when each applies, and how to combine them to cut costs without sacrificing quality.

- **Published:** 2025-11-09
- **Author:** Clixo
- **Reading time:** 6 min read
- **Tags:** prompt-caching, semantic-caching, llm, cost-optimization
- **Canonical URL:** https://clixo.sh/blog/prompt-caching-vs-semantic-caching-llm-apis

The bill comes in and the line item is larger than expected. Someone suggests caching. But there are two very different kinds of caching in LLM systems, and applying the wrong one to a given problem either saves nothing or degrades your product in ways that take weeks to surface.

Prompt caching and semantic caching solve different problems at different points in the request lifecycle. Here is a clear breakdown of both.

## What Prompt Caching Is

Prompt caching is a provider-level feature that reduces the cost of repeated input tokens. When you send a request whose prefix matches a previously cached prompt, the provider charges a reduced price for those cached tokens — typically 10-20% of the normal input price — because it can skip reprocessing them through the attention layers.

The model still runs. You still get a fresh response. What changes is only the pricing for the input portion that matched the cache.

**Providers that support it:** Anthropic (explicit cache-control breakpoints), OpenAI (automatic prefix caching), Google Gemini (context caching with explicit configuration). Details differ, but the mechanism is the same: pay less for tokens the provider has already processed for a previous request.

### When prompt caching is the right tool

Prompt caching is ideal when:

- Your system prompt is long (200+ tokens) and identical across most requests
- You inject a large knowledge base or context document into every request
- You have a RAG pipeline where the same retrieved chunks appear frequently
- Your few-shot examples are fixed and included in every prompt

In these cases, the cacheable prefix represents a large fraction of your total input tokens. Even at 10% of the normal price, the savings compound quickly at scale.

### Limits of prompt caching

Prompt caching does not help when:

- The prompt is mostly variable (user-specific context, per-request data)
- The system prompt is short — the caching overhead may exceed savings on very short prompts
- The model or provider does not support it
- Your deployment does not use sticky routing (some providers require requests to hit the same server for cache hits)

Prompt caching also does nothing to reduce the number of LLM calls. You still pay for output tokens on every request.

## What Semantic Caching Is

Semantic caching operates at the application layer, before the LLM is called at all. It works by storing previous query-response pairs and using embedding similarity to find matches for new queries. If a new query is semantically close enough to a cached one, the cached response is returned directly — no LLM call happens.

The implementation stack: an embedding model to convert queries to vectors, a vector store to store and retrieve them, a similarity threshold to decide when a cache hit is close enough, and a TTL or invalidation strategy for freshness.

### When semantic caching is the right tool

Semantic caching works best when:

- A significant fraction of queries recur with natural paraphrase variation
- Queries are short and self-contained (support questions, FAQ lookups, search queries)
- The response does not depend on context outside the query (no user state, session history, or rapidly changing data)
- You can tolerate slight approximation — a paraphrased query may get a response optimized for the canonical phrasing

Support chatbots, FAQ systems, product search, and internal knowledge bases are the canonical use cases. In these domains, a 30-50% cache hit rate is realistic at scale.

### Limits of semantic caching

Semantic caching introduces risk that prompt caching does not:

**Semantic drift between query and response.** Two questions can be close in embedding space but require meaningfully different answers. "How do I cancel my subscription?" and "How do I pause my subscription?" are semantically similar but the correct responses differ. Your similarity threshold is your primary control over this risk — setting it too high causes incorrect cache hits.

**Staleness.** Cached responses do not update automatically when your product, pricing, or policies change. You need an explicit invalidation strategy. For time-sensitive information, semantic caching may not be appropriate at all.

**Debugging difficulty.** When a user gets a wrong answer via a cache hit, diagnosing it requires tracing through the cache lookup, not just the model call. Add cache-hit metadata to your logging from the start.

## Combining Both Approaches

The two techniques are complementary, not exclusive. A well-architected system uses both:

1. Semantic cache check: if the incoming query matches a cached response above your threshold, return it immediately — no LLM call, no cost.
2. If no cache hit, call the LLM with prompt caching enabled: pay full price for output tokens and variable input tokens, but reduced price for the stable system prompt prefix.
3. Cache the new response for future semantic lookups.

```mermaid
flowchart TD
  A["Incoming query"] --> B{"Semantic cache\nhit above threshold?"}
  B -- Yes --> C["Return cached response\nno LLM call"]
  B -- No --> D["Call LLM with\nprompt caching enabled"]
  D --> E["Pay reduced price\nfor cached prefix tokens"]
  E --> F["Receive fresh response"]
  F --> G["Store in semantic cache"]
  G --> H["Return response to user"]
```

This layered approach handles both classes of cost: recurring queries (eliminated by semantic cache) and repeated context tokens (reduced by prompt caching).

## Choosing Between Them When You Can Only Do One

If your queries are diverse and non-repeating (document analysis, per-user personalization, agentic tasks), semantic caching will have low hit rates and is not worth the added complexity. Use prompt caching to reduce the cost of long, stable system prompts.

If your queries are repetitive and your system prompt is short, semantic caching offers more savings. The math: if semantic caching eliminates 40% of LLM calls entirely, that is more cost reduction than prompt caching achieves on the remaining 60% of calls.

If you are unsure, instrument before choosing. Log a week of production queries, cluster them by embedding similarity, and measure the potential hit rate for semantic caching. If the natural hit rate is below 10%, semantic caching will not move your bill meaningfully.

The right caching architecture depends on your query distribution, your context structure, and your quality tolerance. If you want help designing an LLM cost architecture that fits your specific product, [reach out to Clixo](https://clixo.sh/#contact).

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