Here's a pattern I see constantly in mid-size engineering orgs:

You set up Datadog, Grafana, or your ELK stack. Logs and metrics pour in. 6 months later, you're sitting on 50TB of telemetry data and your cloud bill looks like a phone number.

The thing is — 80-90% of that data is stale. Nobody's querying logs from 4 months ago day-to-day. But you keep it around "just in case" because deleting production data feels wrong.

The real cost isn't just storage. It's the query performance hit, the index bloat, and the engineers who spend hours optimizing retention policies instead of shipping features.

The fix is simple in theory: tier your data. Keep hot data fast and accessible. Move cold data to compressed, cheap storage. Make restores instant when you actually need historical data.

That's exactly what DataHibernate does — automated backup and restore for stale, high-volume data. Small teams are cutting their storage costs by 80%+ without losing access to anything.

If your infra bill keeps you up at night, this might be worth a look.