Comparison
KV Cache vs Prompt Caching
KV Cache and Prompt Caching are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for KV Cache
KV Cache comes up when the question is fundamentally about architecture.
Generating a 4K-token response: the KV cache fills up to 4K entries per layer.
When you would reach for Prompt Caching
Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
A long-context RAG app caches the system prompt + few-shot examples; per-call latency drops from 6s to 1.5s, cost drops ~80%.
Frequently asked
What is the difference between KV Cache and Prompt Caching?
KV Cache: The KV cache stores the key and value vectors of all earlier tokens during generation so the model does not recompute them at every step. It is the main memory cost of LLM inference. Prompt Caching: Prompt caching stores the KV-cache state of a long prefix (system prompt, large document, tool definitions) so subsequent calls that reuse it skip the prefill compute — cutting TTFT and cost by 50-90%.
When should I use KV Cache vs Prompt Caching?
KV Cache is the right concept when you are focused on architecture. Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
Are KV Cache and Prompt Caching the same thing?
No. KV Cache is architecture; Prompt Caching is inference. They are related but address different parts of the AI stack.