Yes—you can run a private, browser-based AI assistant on a Linux VPS. The practical design is a Dockerized Open WebUI interface, connected either to Ollama models running on that VPS or to an OpenAI-compatible remote endpoint. Open WebUI supplies the ChatGPT-like interface; the model backend supplies generation. A reliable deployment also needs a persistent data volume, HTTPS, firewall rules, backups, monitoring, and a tested recovery procedure.
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This guide covers the complete setup, realistic VPS sizing, the local-versus-remote decision, and the security controls required before putting the assistant on the public internet.
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What a self-hosted AI assistant on a VPS actually contains
A VPS deployment is a small web application stack rather than a single executable. Separate the pieces so you know what must be secured and what consumes resources:
- Linux VPS: the always-on server, storage, network interface, firewall, and operating-system account.
- Docker Engine and Compose: the container runtime used to install and update the application consistently.
- Open WebUI: the web interface, user accounts, conversations, settings, file uploads, and integrations. The project describes it as a self-hosted platform designed to run entirely offline.
- Model backend: Ollama on the VPS for local inference, or an OpenAI-compatible endpoint elsewhere.
- Reverse proxy and TLS: the public HTTPS entry point. It forwards requests to Open WebUI while keeping the application port private.
- Operations layer: backups, disk and memory monitoring, update procedures, access logs, and a restore test.
The persistent Open WebUI data volume is not optional. It contains chats, users, settings, and potentially uploaded documents. Recreating a container without that volume can make the interface appear empty even though the container itself starts normally.
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How much VPS capacity do you need?
A 2026 VPS deployment guide gives these starting points: 2 vCPUs and 4 GB of RAM for development or testing; 4 vCPUs, 8 GB of RAM, and at least 20 GB of SSD storage for production. Those are baselines, not performance guarantees. Local model inference can require substantially more memory than the web interface, and the actual requirement depends on model size, quantization, context length, simultaneous users, embedding jobs, and retrieval workloads.
| Use case | Starting capacity cited for 2026 | What it means |
|---|---|---|
| Development or testing | 2 vCPUs, 4 GB RAM | Suitable for learning the stack and one light user; local-model speed may still be poor. |
| Production UI with a modest workload | 4 vCPUs, 8 GB RAM, at least 20 GB SSD | A baseline for the application and a small workload, not a promise of fast local inference. |
| UI-only VPS with remote inference | Potentially smaller than the production baseline | The remote endpoint performs generation, so the VPS mainly serves the web interface, accounts, files, and network traffic. |
| VPS serving local models | Not fixed | Budget additional memory and storage for model files, context windows, embeddings, and concurrent requests. |
Storage must hold the operating system, container layers, model files, Open WebUI data, logs, and backups or a separate backup target. Keep free space available: a full filesystem can stop uploads, database writes, logging, and even normal container startup.
CPU-only inference versus a remote endpoint
A low-cost VPS can host the interface while sending prompts to an OpenAI-compatible service. That arrangement avoids downloading large model files and usually produces better latency than CPU-only inference on a small machine, but prompts and uploaded files leave your server and are subject to the provider’s billing, retention, region, availability, and terms.
Running Ollama locally keeps prompts and files under your control and avoids per-request API charges. In exchange, you pay for more RAM, storage, CPU time (or a VPS GPU where available), model downloads, upgrades, and capacity planning. A model that fits in memory for one user may become unusable when several users, long contexts, retrieval, or embeddings run at once.
| Consideration | Ollama on the VPS | Remote OpenAI-compatible endpoint |
|---|---|---|
| Privacy and data residency | Prompts and files remain on infrastructure you control, subject to your own backups and logs. | Data is sent to a third party; review its retention, region, and processing terms. |
| Recurring cost | No per-token API bill, but larger VPS resources and storage cost more. | Lower VPS resource needs, with provider usage charges. |
| Latency | Can be fast when the VPS has enough compute and is near users. | Depends on the network path, provider region, queue, and model service. |
| Model choice and quality | Limited to models that fit your hardware and software stack. | Access to the provider’s available models and capabilities. |
| Operations | You download, update, monitor, and troubleshoot models. | The provider operates inference; you still manage credentials, failures, and data handling. |
| Reliability | Independent of an external API, but constrained by your VPS capacity. | Dependent on internet connectivity and the provider’s uptime and limits. |
Prepare the VPS before installing anything
Choose the region and disk deliberately
Create a Linux VPS in a region close to the people who will use it. Lower network distance improves the perceived responsiveness of the web interface and remote inference calls. Choose SSD storage large enough for the OS, containers, model cache, logs, and temporary uploads, with additional encrypted storage for backups.
Patch the operating system and create an administrator
Use the provider’s console for the first login, update the system, and create a named administrator instead of doing routine work as root. On a Debian- or Ubuntu-based host, the sequence is:
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sudo apt update
sudo apt full-upgrade -y
sudo adduser aiadmin
sudo usermod -aG sudo aiadminInstall and test SSH keys for the new account before restricting root login. Keep an emergency console path through the VPS provider in case an SSH change locks you out.
Set a host firewall
Allow SSH only as broadly as your administration model requires, and expose web traffic only on the TLS endpoint. With UFW, a basic policy is:
sudo ufw default deny incoming
sudo ufw default allow outgoing
sudo ufw allow OpenSSH
sudo ufw allow 80/tcp
sudo ufw allow 443/tcp
sudo ufw enable
sudo ufw status verboseDo not open Ollama’s port or an unauthenticated Open WebUI port to the whole internet. If your SSH service uses a nonstandard port, allow that port instead of the OpenSSH profile.
Install Docker and create a persistent Open WebUI container
Install Docker Engine and the Compose plugin from your Linux distribution or Docker’s official installation instructions. Confirm that your administrator can run Docker and that the service starts automatically after a reboot:
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sudo systemctl enable --now dockerGenerate and protect the application secret
Open WebUI uses WEBUI_SECRET_KEY to protect application secrets. Generate a long random value on the server, keep it out of source control, and preserve it when recreating the container:
openssl rand -hex 32Store the result in a root-readable environment file or a secrets manager. Rotating it can invalidate sessions and require users to sign in again, so document the change and schedule it deliberately.
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Start Open WebUI with durable storage
The official quick-start pattern publishes host port 3000 to container port 8080 and mounts a persistent open-webui volume. For an initial private test, create the volume and run:
docker volume create open-webui
docker run -d
--name open-webui
--restart unless-stopped
-p 127.0.0.1:3000:8080
-e WEBUI_SECRET_KEY='PASTE_THE_GENERATED_SECRET_HERE'
-v open-webui:/app/backend/data
ghcr.io/open-webui/open-webui:mainBinding to 127.0.0.1 keeps port 3000 reachable only from the VPS, which is appropriate when a reverse proxy will provide HTTPS. If you are testing from a controlled private network and intentionally need direct access, the documented port mapping is -p 3000:8080; do not treat that as a substitute for authentication, TLS, and firewall rules.
Check the container and its logs:
docker ps
docker logs --tail=100 open-webuiOpen WebUI’s data path must remain mounted during every upgrade or recreation. A named Docker volume is convenient; a host-mounted directory can make disk usage and backup policies more visible. Either approach is valid if it is included in backups and protected with appropriate filesystem permissions.
Connect Ollama or a remote model service
Option A: Ollama on the same VPS
Install Ollama using its current Linux instructions, download a model that fits the machine, and verify that Ollama can answer locally before involving Open WebUI. If Ollama is running on the host while Open WebUI runs in Docker, the container must be able to resolve and reach the host. The documented arrangement requires Ollama to listen on the required interface; when Docker needs to reach the host, that commonly means binding Ollama to 0.0.0.0 rather than only loopback.
Expose that listener only to the Docker network or localhost-equivalent path. Do not publish port 11434 to the public internet. Configure Open WebUI’s Ollama connection using its administration settings or the container environment documented for your release, then test model discovery and one short prompt. If the container cannot connect, inspect the bind address, container-to-host route, firewall rules, and the URL configured in Open WebUI before changing model settings.
Option B: An OpenAI-compatible remote endpoint
In Open WebUI, add the endpoint URL and API credential through the administrator connection settings or the corresponding environment configuration for your release. Use a dedicated key with the smallest available permissions, keep it out of shell history and source control, and set provider-side spending or rate limits where available. Test with a non-sensitive prompt first, then confirm which files and conversation data are sent off-server.
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Complete the first-run setup safely
- Open the private URL. Use an SSH tunnel or the reverse proxy’s temporary private route while onboarding rather than exposing an unsecured port.
- Create the first account. The first registered account becomes the administrator in a fresh installation. Use a unique password stored in a password manager.
- Disable public registration. After invited users are created, turn off open sign-up unless anonymous self-registration is an explicit requirement.
- Configure only the required model connections. Remove test endpoints and stale API keys.
- Test from another client. Sign in from a separate browser or device, send a short prompt, upload a harmless test file if uploads are needed, and verify that the expected model responds.
- Check persistence. Restart the container and confirm that the account, settings, and conversation remain present.
Put HTTPS and access controls in front of the application
For an internet-facing deployment, use a reverse proxy that terminates HTTPS and forwards to 127.0.0.1:3000. Point a domain name at the VPS, obtain a certificate from an automated certificate authority, redirect HTTP to HTTPS, and renew the certificate before expiry. The proxy should pass WebSocket or streaming connections correctly, because chat responses may arrive incrementally.
Keep the VPS firewall limited to the proxy’s public ports. The Docker-published application port, Ollama port, database-like files, and Docker socket should not be reachable from the internet. Never expose an unauthenticated model endpoint: strangers can consume CPU, fill disks with uploads, invoke tools or code pipelines, and create unexpected remote-API charges.
Open WebUI’s hardening guidance notes that authenticated users may gain access to inference, tools, and code pipelines. Treat every account as a security principal: use least privilege, restrict administrator access, remove inactive users, review logs, and avoid enabling tools that users do not need.
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Back up the persistent volume
Back up the Open WebUI volume to encrypted storage on a schedule that matches the value of the conversations and uploaded documents. A simple archive example is:
mkdir -p /srv/backups
docker run --rm
-v open-webui:/data:ro
-v /srv/backups:/backup
alpine tar czf /backup/open-webui-$(date +%F).tgz -C /data .Protect the backup directory and copy archives off the VPS. Define retention so old conversations are not kept forever, and include model configuration, reverse-proxy configuration, firewall rules, and secret-rotation notes in your recovery documentation. Do not place live API keys in an unencrypted archive.
Test a restore, not just a backup
Periodically create a disposable volume, extract a backup into it, attach it to a test container, and verify that users, settings, chats, and required files appear. A backup that has never been restored is an assumption, not a recovery plan.
Update with a rollback path
Before pulling a new Open WebUI image or changing model software, record the current image tag, back up the data volume, and note configuration values. Pull the new image, recreate the container with the same volume and secret, and inspect logs before allowing normal users back in. Keep the previous image available long enough to roll back if authentication, streaming, or model connections fail.
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Watch the resources that fail first
- Memory: alert before the kernel kills containers; local models, embeddings, and long contexts can consume it rapidly.
- Disk: monitor model caches, uploaded documents, Docker layers, logs, and backup archives separately.
- CPU and load: sustained saturation explains slow token generation and can starve the web interface.
- Availability and errors: collect reverse-proxy status codes, container restarts, failed model requests, and authentication events.
Troubleshoot the failures you are most likely to see
“The container cannot reach Ollama”
Check that Ollama is running, listening on the interface required by the Docker-to-host arrangement, and not blocked by the host firewall. Confirm the URL configured in Open WebUI from inside the container’s network context. A host-only address such as 127.0.0.1 inside the container refers to the container itself, not the VPS host. Correct the route or bind address, then retry with a small model request.
“The VPS is killed by the out-of-memory manager”
Reduce simultaneous requests and context length, stop unnecessary services, and use a smaller or more aggressively quantized model. If the workload is still above the machine’s capacity, resize the VPS or move inference to a remote endpoint. Adding swap may prevent an abrupt crash, but swapping makes generation much slower and is not a replacement for adequate RAM.
“Conversations disappeared after recreation”
Inspect the container’s mounts and confirm that the same open-webui volume or host directory is attached at /app/backend/data. Restore the latest known-good archive if the volume was deleted or corrupted. Never run an upgrade command that silently creates a new, empty volume name.
“The assistant is publicly exposed”
Immediately restrict the firewall or stop the container’s public port, then put HTTPS and authentication in place. Rotate exposed API credentials, review accounts and logs, and check provider usage for unexpected requests. Disable public registration and unnecessary tools before reopening access.
“Responses are too slow”
Measure whether the delay is in the browser, network, model startup, or token generation. CPU-only inference, a distant region, disk swapping, and an oversized model are common causes. Compare a smaller quantized local model, move the VPS closer to users, reduce context and concurrency, or test a remote endpoint. Do not infer that a larger model is better for your workload if it makes the service unusable.
A production-readiness checklist
- Linux is patched and routine administration uses a non-root account.
- SSH access is key-based and the provider’s emergency console is documented.
- Only required firewall ports are open; Ollama and Docker are private.
- Open WebUI uses a long, unique
WEBUI_SECRET_KEYstored outside source control. - The data volume is mounted, encrypted backups run, and a restore has succeeded.
- HTTPS is enforced, certificates renew automatically, and streaming connections work.
- Public registration is disabled unless explicitly needed.
- Admin access, API keys, tools, and code pipelines follow least privilege.
- Memory, disk, CPU, restarts, authentication events, and model errors are monitored.
- Container updates have a documented rollback and secret-rotation procedure.
Bottom line: choose the smallest architecture that meets your privacy and workload needs
For experimentation, a 2-vCPU, 4-GB VPS can host the interface and teach you the deployment workflow. For a modest production interface, start from the cited 4-vCPU, 8-GB, 20-GB SSD baseline and measure real usage. If you need local inference, size the machine for the model and concurrency rather than for Open WebUI alone. If privacy and predictable local control matter most, run Ollama privately on the VPS; if hardware cost and model breadth matter more, connect Open WebUI to a carefully governed remote endpoint. In both cases, HTTPS, authentication, restricted network exposure, durable storage, backups, and a tested restore are part of the assistant—not optional polish.
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