David Edwards
Aptos Labs and Jump Crypto have launched a decentralized, high-performance cloud storage network named Shelby, aiming to redefine the infrastructure supporting Web3, AI, and decentralized applications. Positioned as a blockchain-native alternative to traditional cloud providers, Shelby marks the industry’s latest push to decentralize cloud computing.
Announced on Tuesday, Shelby is a chain-agnostic, globally distributed storage layer powered by Aptos’ proprietary high-throughput blockchain technology. The network supports compatibility with Ethereum, Solana, and other major chains, enabling developers to build and deploy cross-chain decentralized apps with cloud-like speed.
The Shelby network specifically targets data-intensive applications — including AI workloads, streaming services, and DePIN (Decentralized Physical Infrastructure) projects — where latency, ownership, and control are critical. Aptos’ engineering lead, Pranav Raval, described Shelby as a cloud-grade solution that allows developers to “retain ownership, control pricing, and program business logic at the protocol level.”
Developed by Aptos Labs, known for its high-speed blockchain infrastructure, in collaboration with Jump Crypto, the digital assets arm of Jump Trading Group, Shelby enters a market currently dominated by cloud incumbents like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.
Shelby’s decentralized architecture aims to address the ballooning demand for data storage and processing capacity, fueled in part by the rapid expansion of AI systems. According to projections by the International Energy Agency, global data center energy usage is expected to double by 2030. This growth underscores the need for scalable and efficient alternatives like Shelby.
Raval emphasized Shelby’s role in powering real-time AI data marketplaces, stating, “AI agents aren’t just smart—they’re connected to live, contextual data anytime, anywhere.” He added that Shelby enhances model training capabilities by enabling data to be streamed on demand, creating new efficiencies in AI deployment.
