Comparison
Inference vs Prompt Caching
Inference 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 Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
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 Inference and Prompt Caching?
Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache. 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 Inference vs Prompt Caching?
Inference is the right concept when you are focused on inference. Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
Are Inference and Prompt Caching the same thing?
No. Inference is inference; Prompt Caching is inference. They are related but address different parts of the AI stack.