AI Tokenomics in a Minute
Prompt Caching
How prompt caching can reduce repeated-input cost, and what it does not guarantee.
In one sentence
Prompt caching reuses previously processed prompt prefixes so repeated large context can be billed or computed more cheaply than full reprocessing—when the product supports it and the prefix matches.
Why it matters
Agent sessions often resend large stable context (rules, docs, lockfiles). Caching can cut cost without shrinking useful context—when it actually hits.
How it works
- Stable content at the beginning of the prompt is the usual cache candidate.
- Cache hits typically require matching prefixes and provider-specific rules (TTL, minimum size, explicit cache breakpoints).
- Cached reads are often priced below uncached input; writes may have their own price.
- Volatile content should sit after the stable prefix so it does not bust the cache every turn.
Example
Put long-lived AGENTS.md and architecture docs before the changing user task and tool outputs. If the huge prefix changes every message, you pay as if caching barely exists.
What this proves
Provider usage reports can prove when cache reads/writes occurred and what they cost under current pricing.
What this does not prove
Caching does not prove the answer was correct, that context was necessary, or that total session cost will fall if retries and tool loops dominate output tokens.
Last reviewed 2026-09-06. Title for citation: AI Tokenomics in a Minute: Prompt Caching.