I run infrastructure for mid-market firms and I see the same pattern: Kubernetes adoption creates an explosion of small, changeable artifacts — YAML manifests, Helm charts, CRs, and secrets — spread across clusters, Git repos, and backup targets. That growth drives storage costs, creates audit gaps, and makes refresh and recovery painful. When a single misapplied manifest causes an outage, the cost is not just a few gigabytes; it’s hours of diagnosis, rollback and SLA penalties.

Traditional storage approaches — generic object stores, block arrays, and file servers — were never designed for high-churn, small-file configuration data. They charge per-object, have poor metadata/search, and treat config artifacts the same as VM images. That mismatch inflates bills and slows operations: backups get larger, restores take longer, and auditors demand provenance you can’t easily produce.

The practical response is to treat Kubernetes YAML and related configuration as a first-class data type and manage it with an intelligent data platform like STORViX. That means indexing and deduplicating small files, enforcing lifecycle and retention policies per environment, providing immutable, auditable snapshots, and integrating with GitOps and secrets workflows. The result is tighter control, predictable costs, and far better recovery posture — without pretending a single vendor will fix every problem overnight.