Platform teams: make caching a paved road, not a per-team adventure

Inside any large org adopting LLMs, the same scene repeats per team: someone writes a dedup script, someone wires a vector store, someone files the 'cache poisoning?' ticket nobody owns. Five teams, five half-caches, zero shared learning, and the org's aggregate question corpus, its most cacheable asset, fragmented into puddles.

Crowkis is the paved road version: one cluster (or a few, by isolation needs) operated by platform, consumed by every team over RESP, gRPC, REST, or MCP with golden-path SDK snippets. Virtual keys give each team budgets and rate walls; tenants give them isolation; the dashboard gives platform the org-wide savings number that justifies the road.

flowchart LR
  APP["your app<br/>redis-py · ioredis · Lettuce"] -- "RESP3 :6379" --> CK["crowkis"]
  AGENT["claude code · agents"] -- MCP --> CK
  SVC["services"] -- gRPC --> CK
  CK -- "only on a miss" --> LLM["your LLM provider"]
  style CK fill:#fbe9e8,stroke:#d62221,stroke-width:2.5px

Operationally it behaves like the infrastructure you already run: one hardened container per cluster, Prometheus and OTel into your existing observability, binary-swap upgrades, health checks for your orchestrator. No new operational genre to learn, deliberately.

#The bottom line

The compounding is the prize: every team's traffic warms every other team's hits, institutional answers accrue in one place, and the next team to add an LLM feature starts with a warm cache on day one. That's what paved roads are for.