Launching Now: On-Demand Access to H100s,5090, H200s, B200s & more No more waiting lists. No long-term contracts. No vendor lock-ins. Just instant access to the GPUs you need—from SXM5 InfiniBand to PCIe—all in one place. ⚡ Spin up 100 H100s or reserve a few H200s for training 🧠 Choose across providers without switching tabs 💸 Pay only when you run workloads Your infra team’s dream stack is live. Fire up your first cluster now app.spheron.ai

Enterprise compute. Startup terms. Access H100 clusters instantly with pay-as-you-go pricing - no long-term commits, no minimums, 20-50% cheaper than hyperscalers. Built for AI teams that move fast. → spheron.ai

AWS just raised H200 pricing by ~15%. Our prices stayed cheaper and affordable as always. While hyperscalers keep pushing compute costs up, we’re doing the opposite: giving teams access to the same enterprise GPUs without the enterprise tax. Teams switching to Spheron are already saving up to 80% on GPU spend. The shift happens when people realize: you don’t need hyperscalers to run serious AI.

Most startups think fine-tuning is only for teams with million-dollar ML budgets. That idea did not come from reality. It came from how AI infras has been sold for years. The truth is simpler. Fine-tuning does not need bloated setups, long contracts, or enterprise-only platforms. It needs clear pricing, easy access to GPUs, and the freedom to iterate fast. What actually breaks startup AI budgets is not ambition. It is: - Procurement calls. - Locked contracts. - Unclear pricing. - Slow access to compute. Smart teams are moving away from platforms built for big logos and moving toward infra provider built for speed. Places where they can spin up, experiment, fine-tune, deploy, and repeat without waiting weeks or guessing costs. That is exactly how @spheron is designed. On Spheron, fine-tuning is treated like what it really is: a normal step in building AI. You get real GPUs, transparent pricing, spot and on-demand options, and the ability to start small and scale only when your data proves it makes sense. If your biggest bottleneck is not the model but the cloud around it, the problem is your infrastructure. AI workflows should have worked this way from day one.

Nvidia might be selling shovels in the AI gold rush, but Spheron is selling an entire mining operation, ready to go.

Happy New Year🤍

Why spend $1,000s upfront on buying GPUs, when renting gives you the same compute for ~99% less cost? In a fast AI cycle, renting beats buying for most teams.

Today marks the end of 2025. It was a year of extremes. Compute shortages. Rising costs. Rapid shifts across AI, crypto, and global tech. But one thing stayed constant, Teams that kept building, shipping, and solving real problems moved forward. At @spheron, we spent 2025 doing exactly that. Helping builders access the compute they need, when they need it, without the usual friction. We’re carrying that momentum into 2026. More access. Better pricing. Fewer barriers between ideas and execution. Wishing everyone a strong, focused, and meaningful year ahead. Here’s to 2026 🚀

The direction is clear. Most people will not own serious compute anymore. AI demand is pushing hardware prices up across the board. RAM, GPUs, storage, even consumer devices keep getting more expensive. For many users, owning powerful local machines no longer makes sense. Compute is moving to shared infra that you access when you need it. That shift does not have to mean control, lock-in, or surveillance by default. The real question is who owns and operates that compute. Centralized clouds concentrate power. Open, distributed compute networks spread it out. From our side, the goal is simple. Make high-performance compute accessible without forcing people into a single gatekeeper. If compute is going to be streamed, it should at least be open, affordable, and choice driven.