Comparison
Prompt Caching vs Time to First Token
Prompt Caching and Time to First Token are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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%.
When you would reach for Time to First Token
Time to First Token comes up when the question is fundamentally about inference.
Claude with a 50K-token cached prefix: TTFT drops from ~6s to under 1s on subsequent calls reusing the same prefix.
Frequently asked
What is the difference between Prompt Caching and Time to First Token?
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%. Time to First Token: Time to first token (TTFT) is how long it takes from sending a request until the first response token arrives. The user-perceived latency metric for streaming chat.
When should I use Prompt Caching vs Time to First Token?
Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large. Time to First Token applies when you are focused on inference.
Are Prompt Caching and Time to First Token the same thing?
No. Prompt Caching is inference; Time to First Token is inference. They are related but address different parts of the AI stack.