In the world of Web3, data handling has traditionally divided into two camps: one that must be on-chain, and another that can be avoided if possible.

But here’s the problem— the more complex the application, the more this division appears fragile.

Think about real-world data. It’s never static. Users accumulate behavioral records, AI models continuously iterate parameters, game worlds advance the storyline, and content is being revised and improved. If every change is treated as a brand-new dataset to be forcibly inserted, the system will eventually be overwhelmed by its own history.

Walrus offers a different perspective—view data as a living, lifecycle-managed object, rather than an unchanging stored result. The identity of an object is anchored at creation, and any subsequent changes are just the same object donning new appearances, not new entities emerging out of thin air.

The first change brought by this design is in the referencing mechanism.

When an object’s identity remains stable, upper-layer applications don’t need to readjust addresses, indexes, and permission management with each update. In the long run, this nearly halves the complexity sources of the system.

From a technical standpoint, Walrus’s object storage isn’t just "shoving files onto nodes" casually. It embeds object validation rules at the protocol layer, ensuring each read automatically performs integrity checks. Validation isn’t an extra step after the fact; it’s a core part of the read process.

On the testnet, Walrus has demonstrated support for MB-scale data, ensuring stability through multi-node backups. This means it can handle not only lightweight metadata but also data volumes that approach real application needs.