AI only as good as the data it consumes — and today, that data is trapped in silos. Centralized servers, fragile pipelines, opaque access controls, and single points of failure make large-scale AI development harder than it needs to be. Walrus exists to fix that problem at the infrastructure level.
Walrus is not just another decentralized storage network. It is a purpose-built data layer designed to meet the real demands of AI, media, DeFi, and global applications — scale, availability, verifiability, and fairness.
Storage That Actually Scales
At its core, Walrus is a decentralized object storage protocol built on Sui. Instead of replicating entire datasets across nodes — an approach that quickly becomes expensive and inefficient — Walrus shards data using erasure coding.
RedStuff, Walrus’s encoding engine, applies two-dimensional Reed-Solomon coding. This allows the network to reconstruct data even if multiple nodes go offline, while keeping storage overhead to roughly 4–5x. Compared to full replication, this is dramatically more efficient.
For developers and AI teams, this means large datasets, videos, and archives stay online and accessible even under network churn. Availability is engineered, not assumed.
Verifiability as a First-Class Feature
AI systems need to trust their inputs. Walrus makes data tamper-resistant and traceable by default. Metadata stored on Sui allows nodes to continuously verify integrity, ensuring that what you read is exactly what was written.
Reads and writes are optimized for real-time use cases, making Walrus suitable not just for cold storage, but for live AI workloads, streaming media, and interactive applications.
Encryption is applied end-to-end, ensuring privacy without sacrificing performance.
Native Access Control With Seal
Most storage systems treat access control as an afterthought. Walrus does the opposite.
Seal enforces permissions directly at the storage layer, allowing developers to define who can read, write, or decrypt data from the ground up. With Seal rolling out broadly in September 2025, access rules become programmable, auditable, and enforceable without relying on centralized gateways.
This is already being used to lock down AI training datasets, ensuring contributors retain control while models receive trusted inputs.
Decentralization That Resists Capture
Walrus is designed to avoid the silent centralization that plagues many “decentralized” systems.
Nodes earn WAL tokens based on measurable reliability and availability. Poor uptime results in slashing. Smaller nodes are not disadvantaged, preventing power from concentrating in a few hands.
Data shards are reshuffled across epochs to handle churn smoothly. If nodes fail, the network rebuilds automatically. The system self-heals while remaining globally accessible, allowing teams anywhere in the world to retrieve data instantly.
Turning Data Into Assets
Walrus goes beyond storage by enabling data markets.
Datasets become programmable assets that can be monetized, permissioned, and verified. AI agents gain access to reliable, auditable data streams. Open data marketplaces emerge. DeFi applications benefit from live proofs. Media becomes dynamic instead of static.
Walrus is chain-agnostic. While built on Sui, it integrates with Ethereum, Solana, and other ecosystems, allowing developers to build fully decentralized stacks without traditional servers.
A Growing Ecosystem of Integrations
Walrus is already embedded across diverse ecosystems:
Pipe Network contributes over 280,000 nodes, reducing latency for real-time AI
OpenGradient secures models with permissioned storage
Itheum enables data tokenization and trading
Talus feeds AI agents with verifiable inputs
Linera and Atoma Network extend scalability and onchain logic
TradePort streamlines developer workflows
These integrations demonstrate Walrus’s flexibility across AI, DeFi, infrastructure, and content.
Real-World Adoption at Scale
Walrus is already operating in production:
Alkimi Exchange processes over 25 million onchain ad impressions daily using encrypted, verifiable data
InflectivAI tokenizes gated datasets for contributor-controlled AI training
Tensorblock secures AI models and logic through encrypted storage
Over 20 projects actively use Seal, handling around 70,000 decryption requests
Major content platforms have also migrated:
Team Liquid moved over 50TB of esports archives fully onchain
ZarkLab added AI-powered metadata tagging for instant content discovery
Pudgy Penguins scaled from 1TB to 6TB of assets
Gaming and media projects now protect IP and gameplay logic through programmable access
Built for Developers
Walrus provides production-ready tooling:
TypeScript SDK with Upload Relay
Native Quilt support for efficient small-file handling
Walter dev suite from ETHIndia 2024 winners
Community tools like Threedrive, Seal Drive, Tusknet, and Altlife
The Haulout Hackathon (December 2025) attracted 887 developers, launched 282 projects, and pushed around 20 to mainnet — a strong signal of real builder momentum.
Decentralized Hosting With Walrus Sites
Walrus Sites turn websites into storage objects with unique IDs and URLs. They load directly in browsers, require no wallets, and match traditional hosting costs — while delivering far greater resilience.
Live examples include Flatland, Snowreads, Walrus Staking, and Walrus Docs.
Network Growth and Economics
The network currently stores over 309TB across 3.5 million blobs. Nearly 1 billion WAL is staked, and the largest node controls just 2.6% of capacity — a strong indicator of decentralization.
With a max supply of 5 billion tokens and $140 million raised from Standard Crypto and a16z, Walrus is well-funded and structurally aligned for long-term growth.
Final Thoughts
Walrus transforms data from a fragile dependency into a programmable, verifiable asset. It gives AI systems trustworthy inputs, media platforms durable storage, and developers predictable performance without centralized risk.
This is not speculative infrastructure — it’s already running, already scaling, and already shaping how the AI-driven internet will store and trust its data.
The future of AI doesn’t just need better models.
It needs foundations like Walrus.
