After spending a long time in the crypto space, you will notice a common phenomenon: many teams only think of two numbers when choosing storage solutions—write speed and unit cost.

Initially, it’s necessary to focus on these metrics, but those projects that have survived over a year have learned their lesson—they understand that the real issues are not at the start. The pitfalls come later.

Especially for data stored for half a year or even a year, are you willing to modify it casually? Absolutely not. This situation is too common in projects: during the first three months after launch, iterations run very quickly; after half a year, everything slows down completely. It’s not developers slacking off, but genuinely afraid to touch those core data.

Why? Because the core data of Web3 projects is tied to asset ownership and verification logic. Changing a field could cause a crash; at best, it results in a feature error, at worst, assets could be directly compromised. The cost of making mistakes is unacceptable.

Walrus hits this pain point precisely. Its brilliant design is to give each data object an ID card, and when updating data, it only appends a new version internally, never overwriting the original content. In other words, the history is always preserved, only evolving over time. The benefit of this approach is that the logic of old data remains unaffected, allowing business to continue iterating, and providing a complete chain for audits and traceability.

From the performance on testnets, it can handle MB-level file storage. Even with repeated updates, there’s no need to change reference addresses. With multi-node backups, availability stays above 99%, and read latency is within a few seconds—levels sufficient to meet real business needs.

So my understanding of it is straightforward: this is not a sprinter competing in a speed race, but a solution designed specifically for projects that require long-term secure writes. For projects that treat data security and long-term operation as their lifeline, this design offers a huge upgrade.

Of course, risks are also present. Whether node economic incentives can sustain this historical accumulation mode in the long run is a big question. If incentives weaken later on and many nodes exit, the security of the accumulated historical data becomes a concern.

Overall, Walrus is not suitable for teams pursuing rapid iteration. Its menu only offers one type of customer—long-term projects that prioritize data security above all else.