Platform teams: make caching a paved road, not a per-team adventure
Every product team is duct-taping its own LLM cache right now. Platform engineering exists to end exactly this kind of duplication.
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.
- 1your app · redis-py · ioredis · Lettuce
- 2crowkis
- 3claude code · agents
- 4services
- 5your LLM provider
Four doors in, one cache, and the model only sees genuinely new questions.
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.