Classification and extraction: high-volume, low-variance, born to be cached
Routing tickets, tagging content, extracting fields, LLM classification runs millions of small calls over heavily repeating inputs. The cache hit rate is absurd, in your favor.
Teams increasingly use LLMs as classifiers, route this ticket, tag this listing, extract these invoice fields, because setup is instant and quality is strong. The traffic profile is extreme: enormous volume, tiny outputs, and inputs that cluster brutally. Half of all support tickets are minor variations of the same forty complaints; product listings repeat structures endlessly.
This is the easiest money in caching. Near-duplicate inputs hit semantically; structurally identical inputs hit via templates ('invoice from {vendor} dated {date}' is one pattern, infinite instances); and classification outputs are tiny, so the corpus stays compact while the call volume it absorbs is huge.
flowchart TD Q["incoming query"] --> I["intent classifier"] I --> T["template match"] T --> V["HNSW neighbours"] V --> C["confidence gate"] C --> TR["trust + freshness"] TR -- pass --> A["answer · <1ms"] I -. veto .-> M["(nil) → your model"] T -. veto .-> M V -. veto .-> M C -. veto .-> M TR -. veto .-> M style A fill:#fbe9e8,stroke:#d62221,stroke-width:2.5px style M fill:#f3eee5
Stability is a hidden quality win: cached classifications are deterministic, so the same input never flips labels with model mood, the consistency your downstream automation quietly assumed it had. Confidence gating still routes ambiguous novel inputs to the model, where judgment is actually needed.
The bottom line
Run the math on your pipeline: calls per day, times repeat fraction, times unit price. For classification workloads the repeat fraction is usually the biggest number you'll see this quarter. The dashboard will confirm it within a day.