Sign in to Hivenet
Access your files and manage your storage account.
Store
Access your files and manage your storage account.
Compute
Launch instances and manage your compute resources.
Store and Compute still use separate accounts today. We’re working on a unified sign-in.
Just need to send a file?
Use Send directlyNew to Hivenet? Get Started
Get started with Hivenet
Choose what you want to use first.
Store
Back up photos and files across your devices.
Compute
Launch self-serve GPU compute for demanding work.
Store and Compute still use separate accounts today. We’re working on a unified account experience.
Need Hivenet for business?
Talk to salesNeed to send a file?
Open SendAlready have an account? Sign in
Who runs on Compute with Hivenet
Researchers, startups, studios, and enterprise teams run production workloads on this infrastructure. Not a sandbox.
Who uses Compute.
Used by independent builders, researchers, students, creators, and teams testing ideas before they become larger deployments.
Jupyter notebooks, fast GPU access, green enzymes research.
Hivenet makes our work much easier. With Jupyter notebooks, fast access to GPUs, and reliable infrastructure across regions, we’ve been able to speed up our research on green enzymes for industrial use. It feels like a real step forward in compute platforms.
The possibility of easily obtaining instances with graphics cards for a really cheap price. I use it a few times a month and it's really perfect.
Sustainable, European, sovereignty, fair pricing.
What we like about Hivenet is that it matches how we think about AI: sustainable, efficient, and grounded in Europe. The distributed model gives us security, the option to choose European models strengthens our sovereignty message, and small details like pausing instances or fair pricing just make it practical for us to use day to day.
We've moved over from AWS and GCP to hive. It's a great way to save on costs.
GPU reliability for scaling AI work with students.
Hivenet’s GPUs have been key to scaling our AI work. They let us run advanced models smoothly, so our interactions with students stay fast and responsive. That reliability has made a real difference for us.
Super quick setup. No BS/straightforward pricing. Fast cold starts. Reliable machines.
Built for real AI and compute workflows
Inference
Run inference with managed vLLM. RTX 4090: 737 tokens/s at sustained load. RTX 5090: 45.4ms TTFT.
Training and fine-tuning
Train and fine-tune on 4090 and 5090 instances. Per-second billing. Reusable environments.
Your existing tools work.
Swipe left to see more
Managed vLLM
Serve open-source models without building the full serving layer. Endpoint-compatible with OpenAI-format clients.
Ready templates
Ubuntu, PyTorch, and Jupyter Notebook. Pre-configured. Start in under 60 seconds.
Custom templates
Save your environment. Reuse it on any future instance.
Run where your data must live.
GPU instances available in France, UAE, and USA. Each region is sovereign by architecture — no cross-border data movement, no shared tenancy with other regions. Choose your region at launch.
Specs and pricing
RTX 5090
1 × - 8 ×
VRAM 32 - 256 GB
RAM 73 - 584 GB
CPU 8 - 64
Disk space 250 - 2000 GB
Bandwidth 1000 Mb/s
RTX 4090
1 × - 8 ×
VRAM 24 - 192 GB
RAM 48 - 384 GB
CPU 8 - 64
Disk space 250 - 2000 GB
Bandwidth 125 - 1000 Mb/s
vCPU
2 × - 32 ×
RAM 4 - 64 GB
Disk space 50 - 800 GB
Bandwidth 250 - 1000 Mb/s
Per-second billing. No egress fees. Storage included.
Start self-serve, go deeper when needed
No sales call required for self-serve instances. Docs cover setup, templates, and API references.
Built on distributed infrastructure — sovereign by architecture.
No single data center. No hyperscaler dependency. Cryptographic sharding means no legal pathway for unauthorized data access — not a policy, an architectural guarantee.
Ready to get started?
Try Compute
Launch a GPU instance now. Self-serve. Per-second billing from the first second.
Try Compute
