Storage costs are ridiculously low—99% cheaper than Arweave and even 80% cheaper than AWS. These numbers are indeed eye-catching and hit the market pain points. But we need to see the other side of the picture: when data is retrieved from the hard drive, the real costs start to surface.

In traditional cloud services, egress (data transfer fees) is often more expensive than storage itself. That’s why big companies can easily lock in users—it's easy to get in, but paying to get out is another story. In Walrus’s design, storage nodes earn rent through WAL tokens, which is fine. The problem arises when thousands of users swarm to download the same popular file. A hot decentralized movie suddenly becomes a trending topic across the network, and the nodes’ upstream bandwidth and computing resources are overwhelmed.

At this point, nodes face a real dilemma: either honestly provide high-speed downloads or secretly throttle requests to save costs. If the protocol itself doesn’t incentivize read operations sufficiently, and storage rent can’t cover the bandwidth consumption of high-frequency access, a game begins. What’s the worst-case scenario? Data remains safely stored on hard drives, but at snail’s pace. Users who didn’t pay the "acceleration fee" might experience a black hole with "no return."

That’s why, when developing high-frequency applications, I would never naively rely solely on decentralized storage. My approach is to layer a cache network or retrieval incentive layer in front of Walrus. Walrus guarantees data permanence, while CDN and incentive layers handle traffic surges. The combination of these two layers can transform distributed storage from an archive into a truly scalable infrastructure capable of supporting commercial traffic.

Ultimately, don’t be fooled by the word "cheap." The economic principle of read-write asymmetry applies to any storage system—whether centralized or decentralized.