Capability · Comparison
Prompt Caching vs RAG
Prompt caching and RAG are sometimes pitched as alternatives, but they solve different problems. Prompt caching is about reusing the expensive compute of a repeated long prefix — system prompts, tool descriptions, stable context. RAG is about selecting the right chunk of knowledge to include in a context at inference time. Most production apps use both.
Side-by-side
| Criterion | Prompt Caching | RAG (Retrieval-Augmented Generation) |
|---|---|---|
| What it optimizes | Cost/latency of repeated prefixes | Relevance of retrieved knowledge |
| Handles 'which docs'? | No — prefix is fixed | Yes — its whole purpose |
| Handles 'avoid re-processing'? | Yes — KV cache hit | No — every call re-processes retrieved chunks |
| Cost savings | Up to 90% on cached tokens (Anthropic, OpenAI) | Keeps context small vs dumping everything |
| Latency savings | Significant on long prefixes | Doesn't directly affect latency |
| Setup complexity | Low — mark the cached prefix | Medium — embedder, vector DB, chunker |
| Index freshness | N/A | Core concern — handled by index update |
| Works on closed APIs | Yes (Anthropic, OpenAI, Gemini) | Yes (provider-agnostic) |
| Combined use | Cache stable prefix (tools, system prompt) | Retrieve variable chunks |
Verdict
Prompt caching and RAG are not competing strategies — they're complementary layers of the same optimization stack. Cache what's stable (system prompt, tool definitions, long framing documents) and RAG the dynamic parts (user-relevant passages). A typical production prompt looks like: [cached system prompt + tools] + [RAG-retrieved passages] + [user query]. That pattern minimizes cost without compromising freshness.
When to choose each
Choose Prompt Caching if…
- You have a long, stable prefix that every call reuses.
- Latency on long-context calls is hurting UX.
- You want up to 90% token-cost savings on repeat traffic.
- Your system prompt or tool definitions are >1000 tokens.
Choose RAG (Retrieval-Augmented Generation) if…
- Your knowledge base is larger than your context window.
- Knowledge changes regularly and freshness matters.
- You need source citations for auditability.
- Different queries need different passages of a large corpus.
Frequently asked questions
Can I use prompt caching with RAG?
Yes, and it's the best pattern. Cache the stable prefix (system prompt + tools), put your RAG-retrieved passages after the cache boundary, then user query. You get the savings without giving up retrieval.
How long does a cached prefix live?
Depends on provider. Anthropic's ephemeral cache is ~5 minutes; 1-hour caching is available as a paid tier. OpenAI and Google offer similar automatic caching with their own TTLs.
What's the minimum prefix for caching to pay off?
Roughly 1024 tokens on Anthropic; varies by provider. Below that, cache overhead cancels savings.
Sources
- Anthropic — Prompt caching — accessed 2026-04-20
- OpenAI — Prompt caching — accessed 2026-04-20