io.net

AI infrastructure has been running in the dark. Controlled by a handful of players, the industry has been held back by hidden pricing, secretive deals, and locked doors. We built io.net to change that. Distributed compute. Transparent pricing. Open access. For everyone. Everywhere. For over two years we have been working to bring light to the dark. Today with the launch of our new brand design, we are giving form to the idea. The lights are on. io.net

Hyperscaler customers have caught a case of Supply Chain Paranoia. And it's not surprising. AI companies are hoarding GPUs or reserving capacity blocks as a business tactic against competitors. But startups and enterprises don't need billions in the bank to tap into this idle compute. With io.net you bypass the supply chain paranoia. Our decentralized GPU network gives you immediate access to the 95% of idle compute, at prices up to 70% less than hyperscalers. If you want to build and ship without the fear, we've got you covered. Deploy GPUs today, or whenever you need them: io.net/cloud

The idea that AI will concentrate wealth, eliminate jobs, and leave most people worse off is everywhere. But it isn't inevitable. It's a choice. It's a choice being made by giant corporations who are buying up all of the compute power and trying to crush competition for the sake of their own profits. But there’s another way. And that's what @ionet was built for. Instead of AI being powered by a few corporations, it can be powered by a global network of GPUs that are instantly accessible and affordable for builders everywhere. This is AI for the many, not the few. And it's how you turn AI from a threat into an opportunity for everyone. @ionet isn’t just infrastructure it’s a bet on a more inclusive AI economy. nytimes.com/2026/04/30/opi…

If you're building an AI project, if you blink you may miss your opportunity. To get your product to market and build a sustainable business, you don't have time to wait for what's next. You need infrastructure that is production ready, flexible, and scalable today. While some platforms are still proving their models on testnet, @ionet has thousands of GPUs across 138 countries ready right now. No waitlists. No geographic restrictions. 70% cheaper than Big Tech. Check out the full comparison between io.net and @gensynai: io.net/blog/io-vs-gen…

Both @OpenAI and @AnthropicAI keep warning us about the dangers of AI. Then they keep building it the exact same way. More voices. More perspectives. More ideas. More participation. More transparency. That is what AI needs to counteract the negative influence of giant corporations. The only way to get there is to make AI accessible and affordable to as many people as possible. That's what @ionet is here for. 70% cheaper than AWS. Zero waitlists. Full transparency. And no predictions about the end of the world. bbc.co.uk/future/article…

Centralized compute for AI isn't working. $650B is being spent on infrastructure, but half of US data centres planned for 2026 are delayed. It's clear something needs to change. Check out @ionet CMO, @jack_ionet break down what went wrong, and what can be done about it, in the latest episode of Crypto Coin Show.

It's always important to use the right tool for the job. Particularly with AI. Using the right infrastructure means the difference between quickly getting your product to market, or getting hit by high costs, slow speeds, and an inability to scale. Render Network built a marketplace for 3D rendering jobs. io.net built infrastructure for training and inference at scale. The difference matters. When your agent needs GPU clusters in seconds, not next week after manual coordination, decentralized orchestration isn't optional. It's a must have. Find out more in our latest comparison: io.net/blog/io-vs-ren…

"For my team, the cost of compute is far beyond the costs of the employees". That's a quote from the vice president of applied deep learning at Nvidia Just let that sink in. A senior leader at the world's largest AI chip maker is admitting that currently AI is so expensive that it's more efficient to have employees. If massive enterprises can't afford to use AI, startups and growing businesses don't stand a chance. Unless you change the model. @ionet's distributed compute newtork offers the same leading GPUs and AI models. But at prices that are 70% less. And without the waitlists. axios.com/2026/04/26/ai-…

Trust. It's not something we think about when are busy building. But it is something that comes into focus when things go wrong. AI outages are a warning sign, not the problem. If one provider going down halts your entire pipeline, you should stop to think what else are you trusting them with. Confidential compute combined with decentralization can help ensure your project keeps running, and your data stays safe, without having to put your trust in big tech companies. Find out more: io.net/blog/confident…

We've got some exciting news! @ionet has become the first provider to launch DeepSeek V4 Pro on @OpenRouter. Looking for something to do this weekend? Give it a try for yourself: openrouter.ai/deepseek/deeps…

Major tech companies are laying off up to 10% of their workforce, and blaming AI. They are spending hundreds of billions of dollars on new data centers while up to 85% of existing GPUs are being underutilized due to inefficient infrastructure. This isn't a human resources issue, it's what AI looks like when everything happens behinds closed doors and is controlled by a handful of companies. It is AI for the few, not the many. We believe in AI for the many, not the few. @ionet makes underutilized GPUs from around the world instantly accessible at prices that are 70% less than major hyperscalers, so that anyone anywhere can build great products, and sustainable businesses. fortune.com/2026/04/23/met…

Choosing the right GPUs for your project isn't about picking the "best" one. It's about choosing the right one. For the right job. At the right time. Each GPU has a different price/performance ratio. Understanding how to cluster them for your unique workloads can make the difference between burning through your runway, and having the resources to scale your project Our new GPU cluster cheat sheet helps you get it right: • H100 vs A100 vs L40S (when to use each) • Cluster configs that actually work • Networking + NCCL sanity checks • Cost optimization rules that save real money Check out the full guide: io.net/blog/gpu-clust…

According to a recent study, GPU utilization across enterprise servers sits is at 5%. Yes, just 5%. That means 95% of provisioned GPU capacity is not being used. Hyperscalers are putting people on waitlists, costs continue to rise, billions are being spent on new data centers and utilization sits at 5%. Something is very wrong here. We should be increasing access to AI, not hoarding it. That's why @ionet gives you the flexibility to access affordable compute when and how you need it by orchestrating underutilized GPUs from around the world. businessinsider.com/companies-hoar…

@AnthropicAI just secured another $5 billion in funding from @amazon to use on 5 gigawatts of compute. That's enough to money to fund hundreds of startups. And enough power for 3.75 million homes. Yes, million. That's an entire city. So while most developers are struggling to get access to affordable compute, electricity prices keep going up, and chip shortages are getting worse. Amazon and Anthroipic are just taking more for themselves. If you aren't a billionaire, and and don't feel like paying premiums to support them. There's @ionet. 70% cheaper than AWS. No Waitlists. No need to power a small city. arstechnica.com/ai/2026/04/ant…

It all starts with infrastructure. If your agent can't provision its own compute and scale without approval it's like an intern that you always need to supervise. Helpful, to a point. To take your agents from intern to co-worker, you need to start with autonomy at the infrastructure layer. That's why @ionet created Agent Cloud, a new way for agents, and their humans, to fully automate workflows. Find out how to liberate your agents, and your time: io.net/docs/guides/cl…