Why I stopped equating reliability with uptime
For a long time, I judged storage systems by how often they were “up.” High availability sounded like the ultimate goal. If the data was always accessible, everything else felt secondary.
Over time, that way of thinking stopped making sense.
Uptime usually looks great early on, when participation is high and incentives are fresh. The real stress comes later — when activity slows, nodes leave, and nobody is watching closely anymore. That’s when reliability stops being about percentages and starts being about behavior.
Walrus changed how I think about this because it doesn’t treat recovery as a failure state. It treats it as something normal. Data degrades. Fragments go missing. The system doesn’t panic or demand perfect coordination to fix it. Repair is routine, bounded, and predictable.
That matters more to me than “always on” claims. A system that can recover cheaply is often more reliable over time than one that promises perfection until it suddenly can’t deliver.
What I also appreciate is that this mindset shows up everywhere — incentives, governance, access rules. Nothing assumes constant attention. Nothing relies on ideal conditions.
I’ve seen too many systems fall apart not because they lost data, but because fixing small problems became too expensive or too complex. Walrus feels built to avoid that slow decay.
These days, I trust systems less for how they behave at their peak, and more for how calmly they handle things going slightly wrong. That’s where long-term reliability actually lives.