Prompt Caching / Prefix Caching
Definition
Prompt caching uses already processed input parts again, as far as a provider supports this.
Prompt and prefix caching reuse previously computed states of identical input beginnings. This means a model does not have to fully reprocess this common part of later requests. Whether time or costs decrease depends on the provider, model, cache hits, and pricing plan.
Simply explained
How to reuse an unchanged start of a worksheet.
Why it matters in practice
Helps to realistically classify the possibilities, quality and limitations of AI systems.
Example from business
Possible example: A team tests caching for a long, stable system context.
Limits and pitfalls
- Accept savings or availability without checking the specific provider rules.
Origin & history
Current API documentation describes caching of recurring prompt prefixes. Caching does not change model weights and does not equate to the reuse of finished answers.
Use in mid-sized companies
- Possible application in SMEs: A team tests caching for a long, stable system context.
Frequently asked questions
What does prompt and prefix caching mean?
Prompt caching uses already processed input parts again, as far as a provider supports this.
What errors should be avoided?
Accept savings or availability without checking the specific provider rules.
Sources
- Prompt caching · OpenAI, Prompt caching
Sources checked on 09.10.2026. Editorial usage examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: