Prompt Caching in Practice: From 7% to 74% Hit Rate(Inference in Production Series)
What is prompt caching, and how is it different from a KV cache? A KV cache is automatic and scoped to a single request — it’s what lets a model generate tokens incrementally without recomputing attention over the whole sequence from scratch at every decode step. Prompt (prefix) caching is the cross-request extension of that idea: when a later request shares the same leading tokens as an earlier one, the platform reuses the already-computed KV state instead of reprocessing it.
- ▪What is prompt caching, and how is it different from a KV cache?
- ▪A KV cache is automatic and scoped to a single request — it’s what lets a model generate tokens incrementally without recomputing attention over the whole sequence from scratch at every decode step.
- ▪Prompt (prefix) caching is the cross-request extension of that idea: when a later request shares the same leading tokens as an earlier one, the platform reuses the already-computed KV state instead of reprocessing it.
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| Original publisher | DigitalOcean Community Tutorials |
| Canonical URL | https://www.digitalocean.com/community/tutorials/prompt-caching-in-practice-hit-rate |
| Publication time | 2026-07-24T00:00:00.000Z |
| Retrieval time | 2026-07-27T04:44:29.044Z |
| Last seen | 2026-07-27T04:44:29.044Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ZjVJGCNZJ8t8 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Common Questions on this topic? 1. What is prompt caching, and how is it different from a KV cache? A KV cache is automatic and scoped to a single request — it’s what lets a model generate tokens incrementally without recomputing attention over the whole sequence from scratch at every decode step. Prompt (prefix) caching is the cross-request extension of that idea: when a later request shares the same leading tokens as an earlier one, the platform reuses the already-computed KV state instead of reprocessing it. You don’t configure a KV cache; you do configure prefix caching, by structuring your prompt so the stable content comes first. 2.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DigitalOcean Community Tutorials.
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