@Walrus 🦭/acc Decentralized storage is often discussed as a single category, but in practice storage needs vary greatly depending on how applications behave. Some data is static. Some is permanent. Some changes constantly. Understanding Walrus requires understanding which of these problems it focuses on.
Filecoin is designed around large scale storage markets. Providers commit capacity and users pay for long duration storage deals. This works well for datasets that do not change often and for archival purposes.
Arweave focuses on permanence. Data is stored once and intended to remain available forever. This model is powerful for immutable records and historical archives, but less flexible for applications that update state frequently.
Walrus sits in a different category. It is optimized for active application data. Game states update. Assets move. Metadata evolves. Walrus is designed to support this ongoing interaction rather than one time storage.
This difference shapes every design decision. Walrus integrates closely with execution through Sui. Storage references are part of application logic rather than external pointers. This makes decentralized storage feel native instead of bolted on.
The use of erasure coding rather than full replication reflects this focus. Walrus prioritizes scalability and efficiency while maintaining fault tolerance. Retrieval may involve assembling fragments from multiple nodes, but the system remains resilient even under imperfect conditions.
Walrus does not compete directly with Filecoin or Arweave. Each serves a different stage of the data lifecycle. Walrus targets the moment when decentralized applications behave like real systems rather than static experiments.
The WAL token reflects this role. It is tied to storage usage and network reliability rather than abstract governance. As applications store more data, WAL becomes more relevant.
Walrus is infrastructure. When it works, it fades into the background. That is often the strongest signal of a well designed system.
