How to warm an LLM cache during a model migration
How to warm an LLM cache during a model migration. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Topic · 16 posts
Running Crowkis in production: deployment, hardening, observability, budgets, and day-2 work.
How to warm an LLM cache during a model migration. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.
Per-tenant rate limits (CKEYLIMIT), caps requests and tokens per minute per tenant, enforced at the cache before the spend happens. Here's how Crowkis does it and why it matters for cost and safety.
Semantic dedup (CDEDUP), folds near-duplicate answers into clusters and reports the memory reclaimed, best run as scheduled off-peak maintenance. Here's how Crowkis does it and why it matters for cost and safety.
Observability dashboard + Prometheus, shows hit rate, saved spend, safety blocks, and memory pressure live, and exposes Prometheus /metrics, all in the box. Here's how Crowkis does it and why it matters for cost and safety.
Pull, run, first hit in the dashboard, with no config file, no signup, and no environment variables you're required to set. We timed it. It holds.
docker pull, restart, done, no schema migrations, no export/import, no upgrade runbook. The on-disk format is a stability promise, not an implementation detail.
Model migration without a cold cache, canary and migration workflows carry cache value across a model upgrade, so a new model doesn't cold-start your hit rate. Here's how Crowkis does it and why it matters for cost and safety.
The built-in dashboard for humans, /metrics for your Grafana, one JSON line per event for your pipeline, same truth, three consumers, zero adapters.
One container, a PVC, real health probes, hard memory bounds, graceful shutdown. Everything your cluster expects from a tenant that's read the manual.
Slice the traffic, compare against cached baselines, promote or retreat, model upgrades as a controlled experiment with the cache as your measuring instrument.
Providers have incidents; your product doesn't have to. Health-aware backend routing plus a warm cache turns upstream outages into degraded modes users barely notice.
CROWKIS_MEMORY_LIMIT means what it says, no GC mood swings, no mystery RSS, eviction that engages before the kernel has opinions.
Live verdicts, hit-type economics, top misses, safety blocks, tenant accounting, system pressure, what each panel answers and who keeps it open.
Fail-open design means most 'incidents' are the absence of savings, not the presence of errors. Here's the whole decision tree, which fits on an index card.
Exciting infrastructure is a contradiction in terms. Every Crowkis design decision optimizes for the same review: 'it just runs.'
LLM observability: what to actually measure. A practical, Crowkis-grounded take, no hype, just what actually moves cost, latency, and safety.