If you've been running an AI service for any length of time, you've probably hit this question at some point. "Is using an API actually the cheaper option? Or would it be better to just buy a GPU and run it ourselves?" As model performance converges, cost has become the decisive battleground. Teams at every scale are starting to run the numbers on which approach is actually cheaper for their usage volume — and the answer changes significantly depending on how much you're act

Across industries, job functions, and academia, more teams are building their own AI agent assistants and putting them to work. But the longer you run them, the harder it is to ignore one unavoidable reality: cost. An API invoice larger than your monthly subscription fee, quietly accumulating call by call, has become a familiar sight. AI agents don't call a model once per task. They call it tens or even hundreds of times per job -- planning, invoking tools, verifying results,

If you've ever tried serving an open-source AI model yourself, you know the pain. Setting up GPU infrastructure takes longer than choosing the model itself. Provisioning GPUs, configuring environments, scaling with traffic... the road to running a single model is way too long. Air API eliminates that entire process. It's a serverless API service for open-source AI models. No infrastructure to build. Just an API key to get started. Key Features 💡 OpenAI-Compatible Endpoint

As geopolitical conflicts intensify, the limitations of centralized AI infrastructure are becoming clear. This article explores why distributed infrastructure is emerging as a more resilient and necessary approach.

Google’s TurboQuant reduces KV cache memory usage in LLM inference without sacrificing accuracy. Learn why 80GB GPUs were needed—and why mid-range GPUs may now be enough.

If you’ve ever tried to train a large AI model or run complex simulations, you know how crucial GPU power is. But buying and managing physical GPUs can be a headache. That’s where distributed GPU time comes in. Imagine tapping into a vast network of GPUs spread across the cloud, paying only for what you use, and scaling effortlessly. Sounds like a dream, right? Well, it’s not just a dream anymore. In this post, I’ll walk you through everything you need to know about how to pu

AIEEV has launched a newly redesigned website to strengthen brand trust and better communicate its distributed cloud vision. Discover what’s changed and why it matters.

AIEEV has closed its Pre-A funding round with participation from Bluepoint Partners, ROWE Partners, and AI Angel Club, bringing total funding to $1.5M.

AIEEV, a Distributed AI GPU Cloud, Obtains International Information Security Standard ‘ISO 27001’ Certification

AIEEV Selected for LG Uplus’s AI Open Innovation Program ‘SHIFT’

AIEEV Unveils Air Cloud, a Fully Distributed AI Inference Platform Leveraging Idle GPUs

AIEEV Signs MOU with DXWORKS to Build Regional AI Infrastructure

Connecting Idle GPUs to Reduce AI Infrastructure Costs, The Challenge of Distributed Cloud Startup AIEEV