Public cloud vs. VPS: why the VPS wins for AI
Managed cloud services (AWS, GCP, Azure) are powerful but complex to configure and bill by the resource used. A simple Docker container running 24/7 can cost several hundred euros per month on AWS. A VPS with the same resources costs a fraction of that price, with full root access and no billing surprises.
Concrete advantages of a VPS for your AI tools
- Fixed and predictable cost — no billing by resource or execution
- Data that stays on your infrastructure — no transfer to third parties
- Root access — configure the environment exactly as you want
- The ability to run multiple tools on the same server
- No restrictions on the number of requests or users
Which tool for which use?
The choice depends on what you want to accomplish. Here is a quick reference grid based on your goals.
Selection guide
| Need | Recommended tool | Min. VPS resources |
|---|---|---|
| Chat interface with an LLM | Open WebUI | 2 GB RAM |
| Build LLM applications visually | Dify | 4 GB RAM |
| Automate workflows with AI | n8n + AI nodes | 2 GB RAM |
| Orchestrate complex AI agents | OpenClaw | 2 GB RAM |
| RAG pipelines and no-code chatbots | Flowise | 2 GB RAM |
| Multiple tools on a single VPS | Coolify or Dokploy | 4 GB RAM |
Running multiple tools on a single VPS
With Docker, it is entirely possible to run multiple AI tools on the same VPS. A VPS with 8 GB of RAM can comfortably host Open WebUI + n8n + a tool like Dify or Flowise. Use a reverse proxy (Nginx or Traefik) to route each subdomain to the right container.
If you use local Ollama models, plan for a VPS with at least 8 GB of RAM for a 7B model, and 16 GB for a 13B. For larger models or multiple simultaneous models, a dedicated GPU will be necessary.
