Devops services
Devops
Self-Hosted AI: Private LLM Server with Ollama and Open WebUI
- From $199
- 2-day delivery
- 2 revisions
- Secure checkout
Run AI models on your own server so your data never leaves it. I set up Ollama with the right model for your hardware, a ChatGPT-style web interface for your team, an API for your apps, and lock it down so it is not open to the internet.
What's included
- Hardware and model advice: what fits your CPU, RAM or GPU
- Ollama installed with models such as Llama, Qwen, Mistral or Gemma
- Open WebUI with user accounts for your team
- OpenAI-compatible API for your own apps
- HTTPS, login and firewall; no public model port
- Optional: document chat (RAG) over your files
Who this is for
- Companies that cannot send data to public AI APIs
- Teams that want a fixed monthly cost for AI
- Developers who need a private model endpoint
What I need from you
- A VPS or GPU server (I can recommend one)
- What you want the AI to do
How it works
- Message me with what you need. The first consultation is free.
- I confirm the scope and a fixed price before any work starts.
- I do the work and keep you updated in the chat.
- You review it, and I hand over notes, access and anything I changed.
Free guides on this topic
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Questions
Do I need a GPU?
Small models run on CPU for light use. For a team or fast answers, a GPU server is worth it; I size it for you.
Is it as good as ChatGPT?
Open models are very good for many tasks, especially with your own documents, but not every task. I tell you where they fall short before you buy hardware.
What you'll get
- Production-ready implementation tailored to your goals
- Clean, maintainable code following modern best practices
- Deployment to secure infrastructure with monitoring
- Handover with documentation and full access
Tech stack & tooling
- Containers: Docker, Kubernetes
- Cloud: AWS (EC2, S3, EKS)
- CI/CD: GitHub Actions, GitLab CI
- Nginx, SSL and monitoring
How we'll work together
We start with a short requirements discussion, agree on scope and milestones, then move through implementation, review and handover. You get clear visibility into progress and a stable deployment at the end.
