Hyperstack: NVIDIA GPU Cloud Built for Serious AI Work
Hyperstack is a London-based GPU cloud provider that launched in 2023 with a clear focus: give AI and ML teams direct access to NVIDIA hardware without the overhead of hyperscaler complexity. Still in beta, it’s positioning itself as a platform for teams that need real infrastructure control — Kubernetes, Docker, persistent storage, reserved capacity — rather than a lightweight notebook environment.
The platform targets professional workloads from the ground up. You get the building blocks you’d expect for production AI: multi-GPU support, reserved instances for predictable costs, and a proper API. It’s less of a “spin up a Jupyter notebook” tool and more of a “run your training cluster” platform.
Why Hyperstack Stands Out
Hyperstack’s strength is its infrastructure depth for a relatively young provider. Reserved instances mean you can lock in capacity and costs for longer-running projects — something that matters enormously when you’re training large models or running inference at scale. The Kubernetes support is a genuine differentiator at this price tier; many cheaper GPU clouds hand you a VM and wish you luck.
The pricing competitiveness score is notably high, which suggests Hyperstack is deliberately undercutting the major cloud providers to win early customers. For teams running sustained GPU workloads, that combination of reserved pricing and competitive rates is worth paying attention to.
Being headquartered in London also makes Hyperstack worth a look for European teams navigating data residency requirements or simply wanting lower latency to EU users.
Pros
- Strong infrastructure stack: Kubernetes, Docker, multi-GPU, persistent storage all supported
- Reserved instances available for cost predictability on long-running workloads
- Competitive pricing — among the more affordable options for professional GPU cloud
- API access for programmatic control and automation
- Invoice billing available, making it viable for teams with procurement requirements
Cons
- Still in beta — production stability and SLA guarantees may not yet match established providers
- No SOC 2 compliance yet, which may block enterprise procurement in regulated industries
- No spot instances, so there’s no ultra-cheap preemptible option for fault-tolerant workloads
- Ease of use scores low — the platform leans technical; expect a steeper learning curve than consumer-friendly alternatives
- GPU availability data is limited, so capacity in specific regions may vary
Getting Started
- Visit Hyperstack's website and create an account
- Choose between on-demand or reserved instance pricing depending on your workload duration
- Select your GPU configuration and deployment method (VM, Kubernetes, or Docker)
- Connect via the API or web console to launch your first instance
- Mount persistent storage before starting long training runs to avoid data loss on instance termination
Best for: ML engineering teams that need Kubernetes-native GPU infrastructure at competitive rates and are comfortable trading a polished UX for deeper infrastructure control.