Fluence Is Turning the Cloud Inside Out — and Making It Cheaper
In Brief
Fluence, a decentralized cloud layer company, is expanding its GPU capabilities to compete with hyperscalers like AWS and Google Cloud.
“The right moment to expand into GPUs was probably two years ago,” Evgeny Ponomarev says. “AI demand is exploding — and our customers kept asking for GPU capacity. It was an organic next step.”
For years, Fluence has been building a decentralized cloud layer that challenges hyperscalers like AWS or Google Cloud. The company first made its mark with CPU-based compute — powering blockchain nodes and data processing workloads across its distributed network. But now, with AI dominating global infrastructure conversations, Fluence is stepping boldly into GPU territory.
From Nodes to Neural Nets
“Our first product was simple — virtual servers,” Ponomarev explains. “People could run databases, backends, even analytics. But because we’re native to Web3, most of our customers use Fluence to run blockchain nodes.”
Now, many of those same customers need GPU power — not just for AI training but also to support new blockchain protocols that rely on AI-driven processes. “We realized some nodes literally can’t function without GPUs anymore,” he says. “So expanding our infrastructure was both a customer need and a network opportunity.”
Beyond Cost: Choice and Control
Fluence is often described as up to 85% cheaper than centralized clouds, but Ponomarev insists that’s only part of the story.
“Cost efficiency matters,” he says, “but the real value is customization.”
He gives an example:
“Imagine you need ten H100 GPUs sitting right next to petabytes of training data in the same data center. Hyperscalers rarely let you customize setups like that. In Fluence, because we aggregate hardware from multiple providers and data centers, we can actually give you that flexibility.”
It’s a vision of compute diversity — where anyone, from AI startups to research labs, can access the exact hardware they need without gatekeepers.
Bridging Web3 and AI
As decentralized protocols begin to blend with AI systems, Fluence finds itself at the intersection of two massive revolutions.
“Many new Web3 protocols have AI components that require GPU compute,” Ponomarev says. “We’re enabling those networks to stay decentralized — by running their nodes across our CPU and GPU capacity.”
That convergence is more than technical; it’s philosophical. AI, often criticized for centralization, now has a decentralized foundation to build upon.
Vision 2026: The Decentralized Cloud Stack
Fluence’s roadmap reads like a manifesto for open infrastructure.
“By 2026, success means having the minimal viable cloud to serve any customer,” says Ponomarev. “That includes CPU and GPU instances, block storage, load balancing, managed Kubernetes — the full stack.”
But there’s also a tokenized economy developing behind the scenes.
“Today, people stake to secure compute and earn rewards. Next, we’re introducing a stablecoin collateralized by our token, FLT, to pay for capacity directly,” he explains. “We’re also tokenizing hardware itself — so you can invest in certain types of machines, like GPUs, and share in their revenue.”
It’s a complex system, but Ponomarev distills it simply:
“We’re turning compute into an on-chain marketplace — where anyone can participate, provide resources, or use them. That’s the future of the cloud.”
The Bottom Line
Fluence’s expansion into GPU compute isn’t just a feature update — it’s a statement of intent. As AI’s hunger for compute deepens, decentralized alternatives like Fluence are proving that scale, efficiency, and openness don’t have to be mutually exclusive.
“People used to think decentralized infrastructure couldn’t compete with hyperscalers,” says Ponomarev. “Now we’re proving it can outperform them — in cost, flexibility, and resilience.”
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About The Author
Victoria brings an analytical background to the crypto and Web3 space. She covers digital assets, blockchain trends, and artificial intelligence, translating complex developments into accessible editorial content.
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Victoria brings an analytical background to the crypto and Web3 space. She covers digital assets, blockchain trends, and artificial intelligence, translating complex developments into accessible editorial content.
