SQD.ai and io.net: Decentralized Data at Scale

@helloSQD is a decentralized data layer that allows developers to utilize historical on-chain data for a fraction of the cost of centralized providers like Alchemy and Infura. The SQD.ai SDK is designed for high-performance indexing and processing of blockchain data. Developers that leverage its decentralized data lake infrastructure can slash their data extraction costs by up to 90%. The network aims to provide a secure, low-cost data infrastructure for the future of the Web3 and AI agent economy.

Scaling SQD.ai continues to be a priority, and partnering with io.net ’s extensive network of over 300,000 decentralized GPUs spanning more than 130 countries marks a significant milestone for this objective.

io.net Accelerates SQD.ai Data Processing and Indexing

The partnership allows SQD.ai to integrate high-performance computing power into its ecosystem. This improves SQD.ai’s overall capabilities and allows it to handle petabyte-scaled workloads more efficiently, ultimately supporting more advanced AI agent functionalities and dynamic Big Data analytics.

The partnership creates a symbiotic relationship for the two projects and aligns with a deeper ethos of democratizing access to decentralized services. It also helps facilitate the integration of decentralized data offerings across the io.net network. This expands the reach of SQD.ai and allows io.net providers to increase their offerings to include high-quality, readily available data. Ultimately, this makes them more attractive to AI and Web3 developers and thereby increases their overall revenue streams.

The Future of SQD.ai and io.net

The partnership represents the beginning of a more important collaboration between two major Web3 projects. Combining the deep networks of both io.net’s global decentralized cloud compute with SQD.ai ’s decentralized data layer lays the foundation for a truly decentralized apparatus for DeAI and Web3 developers to build on top of.

By combining the two networks, developers can tap into low-cost, high-performing computing resources and access valuable historical on-chain data. Together, they can be easily implemented to create more advanced tools, such as optimized AI training environments and cross-chain data marketplaces.