Jupyter notebook server
Run JupyterLab on a static IP that isn't your laptop. Close the lid, train, come back tomorrow. Pair with a teammate by sharing the same SSH endpoint.
Anaconda VPS Hosting
Anaconda VPS,
conda up in seconds.
Python data-science VPS on AMD EPYC + NVMe. Jupyter, pandas, scikit-learn ready.
Independent cloud, since 2008. From $2.48/mo · root SSH in 60 seconds.
Starting at $2.48/mo · 50% off · No credit card required
~ ssh root@conda-ams-001
connected
root@conda-ams-001:~# conda --version
conda 24.5.0
root@conda-ams-001:~# conda create -n ml python=3.11 pandas scikit-learn -y
Solving environment: done
Downloading and Extracting Packages... done
root@conda-ams-001:~# conda activate ml && jupyter lab --ip=0.0.0.0
[I] Jupyter Server is running at https://0.0.0.0:8888
(ml) root@conda-ams-001:~# _
Anaconda VPS at a glance
Cloudzy runs Anaconda and Miniconda on Linux VPS plans across 12 regions, starting at $2.48 per month. Plans range from 512 MB to 64 GB DDR5 on NVMe storage with 40 Gbps uplinks. Spin up JupyterLab, pandas, scikit-learn, NumPy, and CPU PyTorch in under five minutes, provisioning takes 60 seconds. Cloudzy has operated independently since 2008, serves 122,000+ developers, and is rated 4.6 / 5 by 708+ reviewers on Trustpilot.
- Starting price
- $2.48 / month
- Stack
- Anaconda · Miniconda
- Provisioning
- 60 seconds
- Regions
- 12 worldwide
- Uptime SLA
- 99.95%
- Money-back
- 14 days
Why data scientists pick Cloudzy
A workspace that stays up.
Four reasons your notebooks belong on a real server.
AMD EPYC + NVMe
Latest-gen AMD EPYC, NVMe-only storage, DDR5 memory. pandas reads a 5 GB CSV in seconds, not minutes.
14-day money-back
Try Anaconda on Cloudzy with a real notebook. Cancel inside 14 days if it doesn't fit. No setup fees.
99.95% uptime
Run a long pandas pipeline overnight, come back to a running notebook. Last-30-day SLA tracked at status.cloudzy.com.
Engineers on chat
Stuck on a conda env conflict at midnight? We answer in minutes, engineers, not script-readers.
Pick your stack
The Python data stack.
Pre-baked, one click.
Anaconda for the full ~3 GB scientific Python distribution, Miniconda if you want lean. JupyterLab listens on port 8888, pandas reads parquet from your NVMe in milliseconds, scikit-learn trains on EPYC cores. CUDA images are on the GPU plans.
conda, pip, mamba, poetry, your call
Anaconda
Full distribution
Miniconda
Minimal installer
JupyterLab
Notebook IDE
pandas
Data frames
NumPy
Arrays · linalg
scikit-learn
ML models
PyTorch CPU
Deep learning
TensorFlow
CPU inference
Use cases
What people run on
Anaconda VPS.
Pandas data engineering
Crunch CSVs, parquet files, and SQL extracts that don't fit in your laptop's RAM. Mount a 1 TB NVMe volume, throw 16 GB at it, finish before lunch.
scikit-learn training
Random forests, gradient boosting, XGBoost on real datasets. EPYC's per-core speed beats laptop CPUs and you can leave a 6-hour sweep running while you sleep.
Quant research
Backtests, factor analysis, Monte Carlo on multi-year tick data. Conda's pinned environment makes notebooks reproducible across the team.
ETL and pipelines
Schedule a conda-managed Python script with cron or systemd, hit an API, transform, write to S3 or Postgres. Cleaner than running it on a laptop.
Teaching and tutorials
Spin up identical Anaconda environments for a workshop. `conda env export` once, `conda env create` for every student. No "works on my machine."
60s
Provisioning
40 Gbps
Uplink
NVMe-only
Storage
12
Regions
99.95%
Uptime SLA
14 days
Money-back
Global network
12 regions. Four continents.
Drop your notebook close to your data.
Train where your data lives. EU, US, ME, Asia, pick the region your S3 bucket or Postgres reads from.
us-utah-1us-dal-1us-lax-1us-nyc-1us-mia-1eu-ams-1eu-lon-1eu-fra-1eu-zrh-1me-dxb-1ap-sgp-1ap-tyo-1
Pricing
Pay for what you use. That's it.
Hourly, monthly, or yearly. No egress fees. No commitments. Currently 50% off all plans.
1 GB DDR5
Quick Jupyter notebooks · Tutorials
$3.48 /mo
$6.95/mo −50%
Deploy now
14-day money-back
- 1 vCPU @ EPYC
- 25 GB NVMe
- 1 TB · 40 Gbps
- Conda / Miniconda ready
- Root SSH · KVM
2 GB DDR5
Pandas / NumPy · Small datasets
$7.475 /mo
$14.95/mo −50%
Deploy now
14-day money-back
- 1 vCPU @ EPYC
- 60 GB NVMe
- 3 TB · 40 Gbps
- Conda / Miniconda ready
- Root SSH · KVM
Most popular
4 GB DDR5
scikit-learn · Real datasets
$14.47 /mo
$28.95/mo −50%
Deploy now
14-day money-back
- 2 vCPU @ EPYC
- 120 GB NVMe
- 5 TB · 40 Gbps
- Conda / Miniconda ready
- Root SSH · KVM
Most popular
8 GB DDR5
DL on CPU · Heavy data jobs
$26.475 /mo
$52.95/mo −50%
Deploy now
14-day money-back
- 4 vCPU @ EPYC
- 240 GB NVMe
- 7 TB · 40 Gbps
- Conda / Miniconda ready
- Root SSH · KVM
FAQ. Anaconda VPS
Common questions, straight answers.
What is an Anaconda VPS?
An Anaconda VPS is a Linux cloud server with the Anaconda Python distribution pre-installed (or one-click ready). You get conda, Python 3, NumPy, pandas, scikit-learn, JupyterLab, and 7,000+ data-science packages, all isolated from your laptop, reachable over SSH from anywhere, and live in 60 seconds.
Anaconda or Miniconda, which should I install?
Pick Miniconda if you want the smallest footprint and only the packages you actually use. Pick Anaconda Distribution if you want the full ~3 GB scientific Python stack ready out of the box. Both ship as one-click images on Ubuntu 22.04 / 24.04 LTS, and you can switch later with `conda install anaconda` or vice versa.
Can I run JupyterLab on a Cloudzy VPS?
Yes. Spin up JupyterLab with `jupyter lab --ip=0.0.0.0 --port=8888`, open the port in the panel firewall, and point your browser at `https://your-ip:8888`. For a private setup, tunnel through SSH (`ssh -L 8888:localhost:8888`), no firewall rules needed and traffic is encrypted.
How much RAM do I need for data-science work?
1 GB is enough for tutorials and small CSVs. 4 GB handles real pandas pipelines on datasets up to a few million rows. 8–16 GB is the sweet spot for scikit-learn on 50M+ rows or PyTorch CPU inference. Anything bigger or you need a GPU, see /deep-learning-gpu/.
Does this support GPU workloads?
Anaconda VPS plans here are CPU-only, they're built for pandas, scikit-learn, and CPU PyTorch/TensorFlow. For CUDA, RAPIDS, or large-scale model training, see /gpu-vps/ and /deep-learning-gpu/. Anaconda installs cleanly on those plans too.
How fast is provisioning?
Once payment is confirmed, your VPS is live in 60 seconds. Anaconda or Miniconda installs in another 2–3 minutes via the one-click image, or under 60 seconds for Miniconda from the curl installer. You'll have `conda --version` working in under five minutes total.
Can I share environments with my team?
Yes. `conda env export > environment.yml` produces a portable file your teammates can recreate with `conda env create -f environment.yml`. The VPS is reachable over SSH so multiple users can pair on the same box, or each can deploy their own from $2.48/mo.
Is there root access?
Yes, every Cloudzy VPS comes with root SSH and full kernel control. Install system packages with `apt`, set up systemd services for long-running training jobs, mount external NVMe volumes, or run Docker alongside conda. No sandbox restrictions.
Can I upgrade if my dataset grows?
Yes. RAM, vCPU, and storage resize live from the panel, most upgrades complete in under 60 seconds with no downtime. Start at 1 GB to learn, jump to 16 GB when your notebooks need it. Hourly billing means you're not locked in to a tier.
Is there a money-back guarantee?
Yes, 14 days from purchase, full refund, no questions asked. Try Anaconda on Cloudzy with a real notebook workload and decide whether the latency, performance, and support hold up against what you've used before.
Ready when you are.
Conda up in 60 seconds.
Pick a plan, pick a region, click. JupyterLab open before your coffee.
No credit card required · 14-day money-back guarantee · Cancel anytime