Welcome to the future
AI infrastructure that fits the whole stack.
Deploy agents, applications, and models across scalable GPU, CPU, storage, and edge infrastructure—without stitching together a pile of clouds.
From code to a live workload
Start with the work
What are you trying to ship?
Choose the problem that brought you here. Each path connects the infrastructure, documentation, and learning resources you need to make it real.
Deploy AI agents
Run agent services, tools, memory, and private endpoints on infrastructure you can actually operate.
- AI agent infrastructure
- private services
Scale GPU workloads
Move model inference, training experiments, and accelerated workloads onto scalable GPU compute.
- GPU cloud
- AI inference
Run CPU services
Deploy APIs, workers, web applications, and the ordinary services every serious AI system still needs.
- CPU cloud
- application hosting
Connect storage
Keep artifacts, data, and application state close to the workloads that use them.
- cloud storage
- data infrastructure
Build at the edge
Design responsive AI experiences that divide work intelligently between the browser, edge, and cloud.
- edge AI
- browser AI
Learn by deploying
Take a hands-on course and finish with a working project—not a folder of slides.
- AI education
- hands-on workshops
Alternate Clouds
AI systems need more than accelerators.
Alternate Clouds pairs scalable GPU access with the CPU, storage, networking, templates, and deployment workflows that turn compute into a working product.
- One deployment flow
- Move from a template or repository to a live URL with less operational glue.
- Workload-shaped infrastructure
- Use GPU where acceleration matters, CPU where it doesn’t, and storage where state must persist.
- Learning built into the platform
- Courses and workshops end in deployments, so teams learn on the same system they can keep using.
One company, three ways forward
Build it. Learn it.Make it work for people.
Alternate Clouds
Deploy agents, applications, models, sites, and services across flexible infrastructure.
Explore the platform EducationHands-on pathways
Build practical AI skills and finish every class with evidence you can publish and share.
See upcoming classes Human-centered AIConsulting
Shape AI products, interfaces, and operating practices around real human needs.
Work with usAnswers for builders
Learn before you choose.
What belongs in an AI infrastructure stack?
A practical look at the compute, storage, deployment, and operational layers between an idea and a production AI system.
02How do you deploy an AI agent?
Follow the path from an agent project to a live, inspectable deployment with the services it needs around it.
03Where does decentralized infrastructure fit?
Understand when distributed infrastructure creates real operational value and when it is only extra complexity.
Good questions
What builders ask us first.
What can I deploy on Alternate Clouds?
You can deploy AI agents, model services, web applications, APIs, workers, static sites, and private services using templates or your own containerized project.
Does Alternate Clouds provide scalable GPUs?
Yes. Scalable GPU compute is part of the platform, alongside CPU and storage. The point is to support the complete workload rather than treating every project as GPU-only.
How is this different from a GPU marketplace?
A GPU marketplace helps you find accelerated compute. Alternate Clouds also gives you deployment workflows, templates, CPU services, storage, domains, and the surrounding infrastructure needed to publish a working system.
Do I need to be an infrastructure engineer?
No. The platform is built for people who need to ship, including designers, researchers, founders, and other technology workers using agents to expand what they can do.
Can my team learn the platform before committing?
Yes. Alternate Futures Education offers practical classes for AI infrastructure, design, HCI, and frontend engineering. Every pathway includes an Alternate Clouds deployment.
Welcome to the future