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Decentralized Cloud Storage Network DataVault Launches: Storage Costs Drop to One-Fifth of AWS S3

DataVault leverages idle hard drive space worldwide to build a decentralized storage network, using erasure coding and encryption to ensure data security and durability.

Decentralized Cloud Storage Network DataVault Launches

On October 30, 2030, decentralized storage company DataVault Labs officially launched the mainnet version of the DataVault storage network. DataVault utilizes idle hard drive space from individuals and enterprises worldwide to build a distributed storage network, with storage costs approximately one-fifth of AWS S3.

DataVault operates on a model similar to Airbnb — users with idle hard drive space can rent out their storage capacity to users who need storage. Data is split into multiple shards before upload, with each shard encrypted and processed using erasure coding (Reed-Solomon encoding) before being distributed across multiple nodes in different geographic locations. Even if some nodes go offline or data is corrupted, the erasure coding can reconstruct the original data from the remaining shards.

DataVault Labs CEO Elena Volkov stated: "Approximately 60% of global hard drive storage capacity sits idle. DataVault enables this idle capacity to generate economic value while providing users with a storage option that is cheaper and more private than centralized cloud storage."

In terms of pricing, DataVault's storage fee is approximately $0.002 per GB per month (compared to AWS S3 standard storage at $0.023 per GB), and bandwidth costs approximately $0.01 per GB. The price advantage primarily comes from the decentralized architecture eliminating data center construction and operations costs.

In its first month since launch, DataVault had approximately 8,000 registered storage nodes with a total available capacity of approximately 50 petabytes. Initial enterprise users include video streaming companies and scientific data archiving institutions.

DataVault's limitation lies in access latency — since data is distributed across multiple geographic locations, first-read latency can reach hundreds of milliseconds, making it unsuitable for hot data scenarios requiring low-latency access.