Gata Taps Walrus to Slash Data Costs and Power Decentralized AI Infrastructure (1)
Staff Writer • 2025-07-22
By eliminating download fees and enabling onchain data validation, this partnership brings open-source AI closer to economic viability at scale Decentralized AI just got a boost where it hurts most—costs. Gata, a company focused on building decentralized large model inference, training, and data pipelines, has partnered with Walrus to eliminate one of the biggest hurdles in the space: prohibitively expensive data access. The integration centers on DataAgent, Gata’s live decentralized data factory. Built to harness idle browser-based compute for AI dataset generation, DataAgent relies on a swarm of contributors frequently pulling large volumes of data. That kind of distributed access has traditionally racked up significant storage and retrieval fees on both centralized cloud and decentralized networks—turning the promise of community-driven AI into a prohibitively expensive experiment. Walrus changes the equation. Unlike traditional storage layers that penalize data retrieval, Walrus offers no download fees, while delivering performance on par with centralized cloud providers. It’s not just about cost savings—it’s about unlocking scale. By integrating Walrus, Gata removes a core bottleneck that has stalled progress in decentralized AI, allowing anyone to help generate synthetic datasets for training large AI models, without worrying about unpredictable fees. “The promise of decentralized AI has been hampered by a fundamental economic challenge: data access is prohibitively expensive,” said Rebecca Simmonds, Managing Executive of the Walrus Foundation. “With Walrus, Gata can now build on a data layer that provides both decentralization and affordability.” From Cost Savings to Compute Transparency But the partnership isn’t just a cost-saver. It’s a strategic infrastructure play. Gata plans to leverage Walrus’s onchain data programmability to validate compute work and automate payments through smart contracts—specifically, Move-based contracts. That means contributors to AI inference and training tasks can be compensated fairly, transparently, and verifiably. This could represent a significant leap beyond the opaque pricing models used by today's AI cloud giants. “Hyperscalers like AWS are extracting 39% operating margins from the $80 billion AI cloud market,” said Oliver, Co-founder at Gata. “This centralized control not only drives up costs but throttles access—stalling AI’s massive economic potential. Our mission is to make AI universally accessible, starting with an open execution infrastructure.” That mission begins with cutting out unnecessary middlemen. With Walrus now underpinning Gata’s DataAgent platform, developers and contributors gain access to an economically sound, decentralized stack for AI execution. More importantly, it lays the groundwork for decentralized model training and inference at scale—something that, until now, has been more buzzword than reality. The Bigger Picture As decentralized infrastructure players continue carving out niches in storage, compute, and execution, the Gata-Walrus partnership is an early glimpse into what a fully open AI ecosystem might look like. Cost-efficient, composable, and auditable from top to bottom. DataAgent is already live. But this is just the beginning. With Walrus in its corner, Gata is positioning itself not just as a project, but as a protocol-level player in the fight to democratize AI. Whether the future of artificial intelligence belongs to a few cloud giants or a global network of contributors might just depend on whether partnerships like this one can scale. If they do, expect the definition of “open-source AI” to get a whole lot more literal.
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