OpenCV VPS Hosting — from €5.49/mo
Process video and images on a server — an OpenCV VPS on NVMe, 5-hour test period, so you risk nothing
Launch OpenCV now
An OpenCV VPS is a virtual server with full root access for computer vision workloads: image processing, video stream analysis and object detection. You install Linux, Python or C++ yourself and build OpenCV with the modules you need. Heavy frame processing runs on a server with spare vCPU and RAM, and NVMe drives keep image sets close at hand. Plans start at €5.49/mo, with a 5-hour test period — check the processing speed on your own data before paying.
Plans
VPS for OpenCV
Image-processing scripts run fine on Standard 1 (4 vCPU, 4 GB) and Standard 2 (4 vCPU, 6 GB). Video streams are CPU-bound — for those take Power 3 (16 vCPU, 24 GB) or higher. 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 OpenCV build
Install Python or C++ and build OpenCV with the modules and contrib packages your vision tasks need.
Processing in the cloud
Image and video analysis runs on a server with vCPU to spare and does not tie up your computer — run pipelines around the clock.
Datasets on NVMe
Fast drives and RAM keep large frame sets and intermediate results right next to the processing.
How to run OpenCV on a VPS
Choose a plan and an image
Pick a plan — Standard 1 (4 vCPU, 4 GB) or Standard 2 (4 vCPU, 6 GB) — and select a Linux image, Ubuntu or Debian, for your vision stack.
Install the OS yourself
Through the panel you deploy the chosen OS on a KVM server yourself — the environment stays fully under your control.
Set up OpenCV
Over SSH or the KVM console install Python or C++ and OpenCV, upload your data and start processing.
A server for vision workloads
We will suggest which plan fits your data volume and load: vCPU count, RAM and NVMe space for frame sets. For GPU model inference we will put together a configuration separately.
We will help build OpenCV and set up secure access. For neural network models see the PyTorch VPS, and for configurations with a graphics card — GPU servers.
Help me size itFrequently asked questions
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Yes. You have root access — install Python or C++ and build OpenCV with the modules and contrib packages you need.
Yes, OpenCV reads RTSP natively. Decoding the stream is the most CPU-expensive operation: 16 vCPU (Power 3) handle several 1080p streams at once.
The base plans focus on vCPU, RAM and NVMe. For model inference on a graphics card we will put together a GPU configuration separately.
Yes. Run detection and tracking on your own data — the compute happens on the server, not on your PC.
Stream processing in OpenCV is CPU-bound: take 16 vCPU or more (Power 3, €27.99/mo). One-off image processing is fine with 8 vCPU.
Over SSH with scp or rsync — traffic is unmetered. Keep frame sets on NVMe next to the processing so the pipeline is not disk-bound.
For classical algorithms — no, everything runs on the CPU. A GPU is needed for real-time neural network detection models — we build such configurations separately.
Yes, at any time from the panel or through the KVM console. Reinstalling wipes the disk, so make a backup copy first.