Walrus: a faster, cheaper, verifiable data layer for the AI and agentic commerce era When centralized clouds shut down, the entire internet halts as well. @WalrusProtocol was designed so data is always live by splitting content across many nodes, anchors “proofs of availability” on-chain, and serves assets through multiple routes. So even when parts of the network fail, apps can still continue running and provenance stays auditable. - - - - - Intro @WalrusProtocol is a decentralized data platform created by @Mysten_Labs, the team behind @SuiNetwork. Data (“blobs”) are erasure-coded and spread across a global set of $WAL-staked storage nodes; metadata, economics, and proof settlement live on Sui. Each blob is mapped to a Sui object (type of value on Sui, addressable on-chain by unique IDs), so ownership and access control become fully programmable → this is where data can interact with smart contracts, payments, and permissions. Tech stack • Red Stuff encoding & sharding: Reduces data replication overhead costs by slicing data into fragments stored across many nodes, rebuilding from subsets if some fail (improves efficiency) • Proofs of Availability (PoA): Nodes are randomly challenged to prove they still hold their data with each signed proof recorded on Sui→ this creates a public, immutable audit ledger of what data is actually available on-chain • Programmability: Transforms data into a programmable asset – enabling automation, monetisation, and access control through Sui smart contracts • Access layer: Portals and “Walrus Sites” provide many paths to publish and get relevant content (no single point of failure) • Cost advantage: About ~80% cost savings are attributed to Walrus’s “Red Stuff” erasure coding and architecture when compared to legacy solutions like Filecoin and Arweave Network stats • Total blobs stored: ~15.45m all-time has been registered, that is actual scale • Utilization: ~538 TB used of ~4,167 TB capacity (~12.9%) • Daily flow: ~5.5k new blobs; 10k–15k storage events • Security: ~1.01B $WAL staked (roughly ~67% of current circulating supply in the market) across ~125 storage nodes Competitive landscape: How Walrus stacks up against Filecoin, Arweave, and IPFS • @WalrusProtocol: Provides verifiable, programmable data management with flexible storage terms; metadata resides on Sui; erasure-coded shards on WAL-staked nodes. Red Stuff encoding delivers lower effective $/GB by avoiding multiple full copies while retaining fast recovery and auditability. • @Filecoin: Offers a market for storage deals (default “cold” archival); miners prove replication and spacetime for term-based contracts. Pay for durable copies (or “forever” persistence), trading off write cost and policy flexibility. Posts zk/PoSt proofs periodically • @ArweaveEco: “Blockweave” for permanent archival; pay once, store forever; proof-of-access built atop PoW. Both Filecoin and Arweave typically pay for durable copies (“forever” persistence), trading off write cost and policy flexibility. Arweave anchors permanence via the blockweave history • @IPFS: content-addressed P2P network; discovery and transport only → persistence requires pinning or external providers. IPFS verifies content hashes on fetch but not persistence In a nutshell, Walrus’ key USP lies in the fact that for apps that need verifiable availability, hot retrieval, and programmable data flows (AI agents, ad performance, dynamic apps), their architecture optimises the cost/performance pain points that general-purpose archival systems or transport layers generally do not optimize for. Adoption and traction for Walrus • @Everlyn_ai (AI video): Using Walrus as the default data layer for video and training artifacts → migrating datasets off AWS/Azure, storing 5k-plus user videos, and leveraging Sui for smart payments (maintain speed while lowering storage costs) • @TalusNetwork (on-chain AI agents): Uses Walrus as its default data layer, so agent state and models have verifiable availability • @flock_io (privacy-preserving AI): Uses Walrus with Seal to enable storing, training, and deploying AI models with privacy guarantees • @Humanityprot (human ID): Uses Walrus to store over ~10m verified user credentials on-chain leveraging Walrus’s verifiable, high-throughput data layer for identity proofs. This integration enables real-time credential issuance, programmable access control, and cross-chain identity verification Recall October’s AWS outage which took many websites offline (since traditional sites cluster in one cloud region). Websites and apps using Walrus are served from multiple decentralized storage nodes with onchain metadata and portal access → thus removing the single-region dependency and keeping content retrievable even when some nodes or regions go dark. This is exactly the class of failure Walrus was designed to avoid. Our personal takeaways • Walrus promises ~80% lower effective storage cost vs. incumbents and more partnerships will only prove that the tech works • There are already live partners across AI/video, agents, adtech, data marketplaces, identity → which is good since there is an actual infra to migrate content off centralized clouds • Walrus’ edge is that it has the capacity to provide verifiable availability at fast speed with programmable economics • Some considerations involve a dependency on Sui as Walrus offloads metadata, pricing, staking, and proof settlement to Sui → which means they inherit Sui’s liveness/fee conditions and governance and any Sui outage or congestion can affect performance. Slashing is also not live yet, so any misbehavior relies more on reputation/economics than hard penalties - - - - - Walrus partner, views are my own