How Walrus Works: The Roles of Users, Nodes, and the Sui Layer

The first time you try to move a large file through a crypto stack, you notice something traders rarely talk about out loud blockchains are great at proving small facts but real products live and die on moving messy heavy data. Images, videos, model checkpoints, audit archives, research PDFs, training sets and logs are what people actually generate. If the system makes that data hard to store, expensive to retrieve, or unclear to own the user leaves quietly. Walrus exists in that practical gap. It is not trying to be another general chain. It is trying to make large unstructured data behave like something markets can reason about, verify, and trade, without pretending the data itself belongs inside a base layer ledger.
At a high level, Walrus splits responsibilities across three roles that matter to investors because they determine where risk sits and who gets paid: users, storage nodes, and the Sui layer. Users are the ones who create demand. In Walrus terms, a user can be a writer who stores a blob, a reader who retrieves it, or a token holder who stakes WAL to help secure the system without operating hardware. Storage nodes supply capacity and availability. The Sui layer acts as the control plane, meaning it keeps the authoritative record of metadata, ownership, and the onchain events that make storage obligations enforceable. Walrus calls itself the data plane and Sui the control plane for a reason the data moves and lives with the nodes but the rights and rules live on Sui.
The cleanest way to understand how it works is to follow one file from the moment it is uploaded. A writer encodes the blob into pieces designed for resilience, then registers the blob on Sui so there is an onchain object that represents it and anchors its metadata. Ownership of that Sui object maps to ownership and control of the blob. The writer then uploads the encoded pieces, often described as slivers, to the storage nodes that have been assigned the obligation to store them. Each node checks what it received and returns a signed acknowledgment. Once the writer collects enough acknowledgments those signatures form a certificate that gets published onchain. That onchain publication marks the Point of Availability, which is the moment the network can treat the blob as reliably available for the specified time window, and the writer can even go offline because the obligation has shifted to the node set.
Reading is intentionally simpler than writing. A reader can query nodes for the blob’s commitments and enough slivers to reconstruct the original data, then verify the reconstruction against the commitments so the output is not just available but also correct. The design goal is that reads do not depend on trusting a single node, and recovery is possible even if some nodes are down or malicious. In the underlying research description, nodes also watch the chain for PoA events and can recover missing slivers after the fact, which matters because it reduces fragility when the node committee changes over time.
This is where Sui’s role stops being abstract and becomes the core of the system. Sui is not storing the blob, it is enforcing the ledger of who owns it, how long it must be stored, and what state transitions count as valid. The Walrus team frames it plainly: every stored blob is represented as a composable onchain object on Sui, and Sui is the canonical source of truth for that metadata. That design gives builders something they can program against. Instead of saying “the file is somewhere,” a smart contract can reference an owned object whose existence and state are verifiable, and that object can become part of application logic.
For traders and investors, the economics sit in the middle of the story, not at the beginning, because the market only matters if the workflow is credible. On Walrus mainnet, users pay two distinct costs: WAL for storage operations and SUI for the onchain transactions required to register and certify blobs. The documentation is explicit that storing a blob can involve up to three onchain transactions, and that Walrus blob metadata lives as Sui objects, which ties some cost to Sui’s storage fund mechanics and rebates. It is also explicit that the encoded size used for cost calculations can be materially larger than the original blob, about five times larger plus metadata overhead, and that per blob metadata can be large enough that small blobs are disproportionately expensive unless users batch them. Walrus even ships a native batching tool called Quilt, and the SDK notes that batching files together is currently recommended for efficiency.
WAL is the payment token, and Walrus states that its payment mechanism is designed to keep storage costs stable in fiat terms, distributing prepaid WAL over time to nodes and stakers, with delegated staking underpinning security. The official token page also discloses a 10 percent allocation for subsidies aimed at early adoption, and it describes slashing and partial fee burning as incentives against low performance and malicious behavior. Those are meaningful long term commitments because they set expectations about how the protocol intends to keep node supply reliable while demand ramps unevenly.
As of today, market pricing suggests the token is trading in the low teens of a US cent, with aggregators showing roughly $0.12 per WAL, around $20 to $25 million in 24 hour volume, and a market cap in the high hundreds of millions depending on source methodology and circulating supply assumptions. This is the point where a sober investor should pause and separate protocol function from token performance. A storage protocol can be technically sound and still struggle if it cannot keep paying users over multiple billing cycles.
That is the retention problem, and it is not theoretical. Storage is recurring by nature, but user behavior is not. People upload once, test once, then revert to what feels easiest. Retention fails when workflows are confusing, when costs are hard to predict, when wallets and signatures create friction, or when developers cannot make storage feel native inside an app. Walrus is clearly aware of this. The SDK explicitly breaks the write process into steps for browser environments because wallet popups can be blocked if they are not tied to direct user actions, which is exactly the kind of small UX detail that kills repeat usage. The docs also provide a cost calculator and optimization guidance because surprises at the moment of payment are another reliable churn trigger.
A real world example makes the tradeoffs concrete. Imagine a small research desk that records trader commentary, screenshots, and execution notes as evidence for post trade review, and then shares a subset with clients. Centralized storage is easy until you need to prove what existed at a specific time, or you need portable ownership across tools, or you need to stop a platform from becoming a silent gatekeeper. With Walrus, the desk could store the archive as blobs whose ownership is represented by Sui objects, then share access by sharing object ownership or controlled references at the application layer. The value is not that the files are magical, it is that the existence, ownership, and availability commitments can be verified without trusting a single vendor. The cost is that you now manage two tokens, user signatures, lifecycle decisions like when to burn objects for rebates, and the operational discipline to batch intelligently.
If you are evaluating Walrus as a trader or investor, treat it like infrastructure, not a narrative. Track whether storage demand grows in a way that looks sticky, whether node performance incentives are producing consistent availability, and whether developers are building experiences that hide the underlying ceremony from end users. Use the public docs and the cost calculator to model what a real application would pay at different blob sizes and durations, then sanity check whether those numbers compete with the alternatives for the segments that actually pay.
Do not outsource your conviction to price charts. Read the Walrus docs, run a small experiment storing and retrieving the kind of data your workflow genuinely produces, and map the user journey from first upload to the second month of renewals. If the system can make that second month feel effortless, retention follows, and in storage, retention is the whole business.
@WalrusProtocol $WAL #Walrus