Walrus does not present itself as a clean or elegant system. It feels more like a working harbor than a showroom. Things move, some parts lag, a few machines cough under pressure, and yet the cargo keeps arriving. That matters more than appearances.
On March 30, 2025, the network was running five nodes holding roughly 220 terabytes of data. During a sync, one node went down completely. Two others fell behind. Anyone who has managed servers knows that quiet moment of tension, watching dashboards update slower than usual, wondering what breaks next. But nothing dramatic happened. Retrieval continued. Even under heavy load, 99.6 percent of the data was recovered. Not because of optimism, but because the system was built for that exact kind of failure.
Walrus does not rely on perfect machines. Data is split into pieces and spread across hundreds of nodes. There is no single full copy sitting somewhere fragile. If close to 40 percent of nodes fail at once, the data can still be reconstructed. It is a bit like tearing a book into pages and storing them across many rooms. You do not need every page in every room. You just need enough of them to read the story again.
The chain that coordinates this process is Sui. It handles rules, incentives, and penalties. Walrus nodes handle the data itself. The separation is intentional. It keeps things lighter, cheaper, and easier to reason about when parts misbehave. Nodes stake WAL to participate. When they stay online and do their job, they earn rewards. When they do not, penalties follow. It is not moral. It is mechanical.
Storage costs sit around 0.004 SUI per gigabyte per month. That low price changes behavior. A research team recently pushed around 1.2 petabytes of information into Walrus. Market snapshots, public conversation data, onchain flows. They avoided traditional cloud storage because access would have been restricted, visibility limited, and costs unpredictable. With Walrus, they could see where data came from and pay only for what they actually used.
Retrieval speeds are usually steady. Pulling around 10 gigabytes takes two to three seconds under normal conditions. Sometimes it is slower. Congestion happens. Nodes can lag. Errors appear. Walrus does not hide this. It accepts messiness as part of operating at scale.
One of the quieter shifts happening on the network involves artificial intelligence agents. These agents store memory on Walrus. Past actions, previous conversations, historical context. Instead of forgetting everything between runs, they remember. That memory can be recombined with onchain logic, analytics, and smart contracts without a central owner deciding who is allowed to look. Large training datasets, embeddings, proofs, and media all live in the same place, shaped and reshaped as needed.
The token side is deliberately slow. The supply starts at 690 million WAL, with distribution spread across community members, contributors, users, subsidies, and investors. Unlocks stretch out until 2033. Predictability matters here. The network needs operators who think in years, not weeks. Demand is meant to come from usage rather than attention.
None of this means Walrus is risk free. Node adoption is uneven. Some operators run powerful machines, others struggle. Downtime still happens. Recovery usually works, but not invisibly. As the network grows, coordination complexity increases. Cheap storage attracts heavy workloads, and heavy workloads expose weak infrastructure faster than marketing ever could. There is also the long-term challenge of aligning incentives as usage patterns shift in the AI era.
Still, partial failures do not break the system. That is the baseline. Everything else builds on top of it. Nodes drop, data survives. Retrieval stutters, blobs remain. Most of the time, nobody notices. The network hums quietly, doing what it was designed to do, and that quiet persistence may be the most honest signal it can offer.