Anaconda Hosting on Your Own VPS
Conda environments and Jupyter on a server, for work that outlasts a laptop session.
Size a server for Anaconda
Every stop is a real configuration you can deploy. Priced per 30 days, billed by the hour, exclusive of 18% GST.
What Anaconda is
Anaconda is a distribution of Python and R for scientific computing, built around the conda package manager. Its value is that conda installs compiled dependencies — BLAS, CUDA libraries, GDAL — as binaries rather than expecting you to build them, which is the difference between a five-minute setup and an afternoon.
The reason to run it on a server rather than a laptop is duration. Training runs, large joins and long simulations do not survive a closed lid or a commute. On a VPS a notebook keeps running, a job started on Friday is still going on Monday, and the dataset lives next to the compute instead of being copied to it.
It is also how a team shares an environment. One conda environment on one server, reachable by everyone, removes the class of problem where results differ because someone has a different NumPy.
Why run Anaconda on a VPS
Long-running jobs survive disconnection — the server does not sleep.
Root access, so system libraries conda cannot supply are still installable.
Data sits on the same machine as the compute rather than crossing a network per query.
Resize when a workload outgrows the plan, without rebuilding the environment.
Anaconda features
Conda environments with reproducible specifications
The scientific Python stack — NumPy, pandas, scikit-learn, SciPy
JupyterLab over your own domain
R alongside Python in the same distribution
Binary packages, so compiled dependencies do not need building
Per-project environments that do not interfere
Recommended server
A starting point for Anaconda, not a hard floor — size it on the sliders.
| Memory | 8 GB — pandas holds working sets in memory and is the usual constraint |
|---|---|
| CPU | 4 vCPU; many scientific libraries parallelise well |
| Storage | 80 GB — conda environments are large before any data |
| Operating system | Ubuntu 22.04 LTS |
How deployment works
Anaconda or Miniconda is installed with a base environment and JupyterLab served over TLS on your own domain, behind authentication. Notebook servers should never be exposed without it — an open notebook is arbitrary code execution as whichever user runs it.
What people run it for
Long training runs
Jobs that take hours or days, on a machine that stays awake.
Shared team environment
One environment specification, so results do not differ by laptop.
Data close to compute
Large datasets on the same disk as the analysis.
Remote notebooks
JupyterLab reachable from anywhere, with the work living on the server.
Anaconda hosting questions
Related applications
LM Studio Hosting Serve open-weight models from your own machine, with an OpenAI-compatible API. 
n8n Hosting Workflow automation with unlimited executions and your credentials on your own server. 
Apache Airflow Hosting Data pipeline orchestration with DAGs and retries, without per-task pricing. 
PostHog Hosting Product analytics and session recording with event data that never leaves your server. 
OpenClaw Hosting A private AI agent instance with its own sandbox and persistent memory, on hardware you control. Deploy Anaconda on your own server
Root access, your choice of size, and no container between you and the application.