If you run ZFS at scale you already lean on zpool iostat as the first and last line of defence for performance issues. The problem is operational: zpool iostat gives useful instantaneous counters (IOps, bandwidth, latency, per-vdev stats) but not the context you need to make durable decisions. Short sampling windows, noisy bursts, ARC/cache interactions, resilver/scrub effects and workload mix all conspire to make that output ambiguous — so teams either overreact (buy more spindles or larger arrays) or under-react (accept SLA risk).

Traditional storage approaches — black‑box vendor arrays, LUN abstraction that hides topology, and run‑to‑failure refresh cycles — amplify that ambiguity. They reward reactive spending and add hidden operational risk during rebuilds, upgrades and compliance events. The smarter shift is to treat zpool iostat as one signal in a broader telemetry and lifecycle system. Platforms like STORViX ingest and normalize ZFS telemetry, correlate it with workload, capacity and compliance timelines, and turn noisy counters into concrete risk and cost decisions: when to rebalance, when to add cache vs spindles, when a vdev is a long‑term liability, and how to plan refreshes to minimise capex and service disruption.