AirCloud got a major update in June 2026. This release focused on improving the experience for every team — whether you're just getting started with AirCloud or running multiple projects every day. We redesigned the console, introduced real-time notifications, and expanded persistent volume support to cover more deployment environments. The goal was to make the platform feel faster and more intuitive at every step. For detailed usage guides, visit AirCloud Docs. This post cov

Every GPU experiment starts the same way: before you write a single line of code, you're already fighting your environment. Matching CUDA versions, installing drivers, resolving package conflicts — two hours gone before anything actually runs. Air Cloud solves this by letting you deploy a container with PyTorch and CUDA pre-configured, then connect via SSH immediately. No local setup required. Add Claude Code into the mix, and you can start writing, debugging, and running cod

AirCloud's April release is built around one goal: making it faster to run AI workloads, more reliable to operate them, and more flexible to put your existing GPU resources to work. This update includes enhanced Air Container operations, the general availability of Air API, Resource Provider (RP) support, and the introduction of an intelligent scheduler. Developers can now handle container access, log monitoring, error response, and API integration more seamlessly. Enterprise

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

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