Jupyter Notebook Server — from €5.49/mo
Run Jupyter in the cloud and compute without limits — a server for Python and your data, 5-hour test period, so you risk nothing
Launch Jupyter now
A Jupyter server is a VPS with full root access where you bring up Jupyter Notebook or JupyterLab and open it from the browser. The key resource here is memory: pandas holds the whole dataset in RAM, so for mid-size data analysis people take Power 1: 10 vCPU, 16 GB RAM, 160 GB NVMe at €17.99/mo, while study projects and light compute fit Standard 1 at €5.49/mo. The computation keeps running on the server even after you close the browser — the main difference from running locally. A 5-hour test period before payment is available — run your notebook on real data first. The full line-up is on VPS hosting.
Plans
VPS for Jupyter
Study projects and light compute fit Standard 1 — 4 vCPU, 4 GB RAM, 50 GB NVMe at €5.49/mo. For large datasets people take Power 1 (10 vCPU, 16 GB) at €17.99/mo or Power 3 (16 vCPU, 24 GB) at €27.99/mo — in data analysis RAM matters more than core count. 5-hour test period before payment.
Compare all plansStandard 1
| 4 vCPU · AMD EPYC |
| 4 GB RAM |
| 50 GB NVMe |
| Unlimited traffic |
| 1× IPv4 · KVM |
| 24/7 support |
Standard 2
| 4 vCPU · AMD EPYC |
| 6 GB RAM |
| 60 GB NVMe |
| Unlimited traffic |
| 1× IPv4 · KVM |
| 24/7 support |
Your own Python environment
Install any versions of Python, R and libraries — NumPy, pandas, TensorFlow, PyTorch — for your data analysis tasks.
Compute in the cloud
Heavy computation runs on the server and does not tie up your laptop — access the kernel from the browser around the clock.
Full control
Root access, any packages and kernels, your own library versions and server snapshots on your own schedule — the environment is entirely yours.
How to run Jupyter on a VPS
Choose a plan and an image
Size the configuration by data volume — from Standard 1 for study projects to Power 1 and up for large datasets — and select a Linux image for your stack.
Install the OS yourself
Through the panel you deploy the chosen OS on a KVM server yourself — so the configuration stays fully under your control.
Set up Jupyter and Python
Over SSH or the KVM console install Python, JupyterLab and the libraries you need, then open the notebook in the browser.
A server for your ML workloads
We will suggest which configuration fits your dataset and model size: RAM volume, vCPU count and NVMe space for data and checkpoints. A practical rule — the dataset should fit into memory with roughly a two-fold margin.
We will help set up secure remote access to Jupyter and HTTPS. For training on graphics cards there are GPU servers, and an overview of machine learning workloads is on AI/ML VPS.
Help me size itFrequently asked questions
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Any with Jupyter kernels — Python, R, Julia and others. You install the kernels and libraries you need on your own server.
These plans come without a GPU — they are sized for CPU compute. For training neural networks on a GPU there is a separate line of servers with graphics cards.
For mid-size data analysis 10 vCPU and 16 GB are enough — Power 1 at €17.99/mo. Pandas holds the dataset in memory, so RAM matters more than core count.
Yes. Once JupyterLab is installed, the notebook opens in the browser at the server address — the compute runs on the VPS, not on your PC.
Through an SSH tunnel or Nginx with a password and TLS. A public Jupyter with no password is direct access to code execution on the server — never do that.
Yes, the computation runs on the server and continues when you close the browser. That is the main advantage over running locally on a laptop.
Yes, at any time from the panel or through the KVM console. Reinstalling wipes the disk, so make a backup copy first.
Notebooks and data live on the server disk, and you take a snapshot of the whole machine yourself in the control panel in a couple of clicks. Automatic backups are not part of the plan.