io.net blog

Your GPU data center investment framework. Compare TCO for cloud, colo, & workstation, including power, cooling, ROI, and hidden costs.

Google Cloud Platform (GCP) has emerged as a formidable AI infrastructure provider. It’s done this by leveraging Google's decades of machine learning expertise and proprietary TPU (Tensor Processing Unit) technology. Boasting Vertex AI, BigQuery ML, and tight integration with TensorFlow and JAX, GCP offers a compelling ecosystem for AI teams already invested in Google's toolchain, as well as a compelling alternative to other hyperscalers like AWS and Azure. . When evaluated purely on GPU comput

Amazon Web Services (AWS) pioneered cloud computing in 2006 and, not surprisingly, remains the dominant player to the tune of 32% market share and $90B+ annual revenue. AWS offers the most comprehensive cloud ecosystem spanning compute, storage, databases, machine learning services, and 200+ integrated products. If you’re an enterprise with complex multi-cloud strategies, AWS will probably be your best option for its unmatched breadth and maturity. Yet, AWS is a wounded giant of sorts. In the G

Akash Network launched in 2020 as the "Airbnb for cloud compute”. In doing so, it pioneered the DePIN movement with a decentralized marketplace for spare CPU and storage capacity.  Fast forward to 2026. Akash now offers GPU support that enables it to compete in the exploding AI infrastructure market. But Akash’s CPU-first architecture and container-focused approach creates some fundamental limitations, especially for startups running large-scale AI training and inference. io.net was purpose-bu

See how Leonardo.Ai scaled from 14K to 19M users and cut GPU costs by over 50% using io.net's high-performance, affordable compute solution for generative AI.

If you’re currently scaling your AI product, you’ve probably noticed something rather unsettling: your infrastructure bill is growing faster than your product. Many startup teams are experiencing compute costs that consume 50-60% of their entire operating budget. That’s more than salaries for engineering, customer acquisition, and other team roles combined.  Let’s be clear: the economics of AI budgets are now, in an ironic feedback loop, threatening the stability of the entire AI sector. Don’t

Vistara Labs used io.net to scale its Zaara AI platform, building 5,600 apps in two months while cutting compute costs by 3x and achieving zero infrastructure failures.

Gensyn is well-known as the GPU solution for "research-first" and "protocol-first" AI developers. Built atop a custom Ethereum rollup, Gensyn is pioneering something genuinely novel: a fully decentralized, trustless network for machine learning computation, where workloads are verified and coordinated across any device on the planet. By any device, it could be consumer laptops, enterprise data center GPUs, gaming hardware, or even a Mac Mini with Apple Silicon chips (M1, M2, and M3), all without

Render Network has built a compelling reputation as the GPU solution for "creative-first" and "research-first" developers. With a decentralized marketplace for GPU compute, native support for Blender and Cinema 4D, and an expanding AI inference layer through its Dispersed subnet, Render Network is a strong fit for 3D artists, VFX studios, and AI/ML teams looking for cost-effective alternatives to centralized cloud providers. Render Network does this all without managing any raw compute infrastru

Over the recent months we have seen both AI providers and hyperscalers go offline for several hours. Production workflows stalled almost immediately. Customer service bots went dark, code pipelines froze, and engineering teams struggled to come up with emergency plans most of them hadn’t prepared for.  Every time there is an outage with a compute provider or massive AI company, there is  an important question that isn’t answered when the service comes back online: if these providers can't guara

Most developers don't fail at distributed GPU training because they select the wrong model architecture. On the contrary, they misstep when provisioning the wrong cluster and GPU mix, wrong interconnect topology, and wrong scaling strategy. To add insult to injury, they’ll burn $4,000 in three hours trying to figure what the heck went wrong.  This quick guide exists so you can avoid this mess.  When we published a GPU cluster quick-reference card on X earlier this quarter, it became one of our

18 production-ready AI agents for NLP, market data, & automation on io.intelligence. Consolidate your AI stack with one API.