AI apps managed around open-source control

Pick the app that matches the job. Open WebUI for private team chat, AnythingLLM to search your own documents, LibreChat for teams that use several AI providers, Flowise and n8n to automate tasks, and ComfyUI for AI image creation.

  • Private AI built on Ollama, the open-source standard
  • Open-source hosting managed with CyberPanel
  • Domains, support, and clear ways to get started

vLLM is an advanced speed option — we only suggest it after testing on your setup.

Managed Local AI

Your own private AI running on your GPU, with Open WebUI — a ChatGPT-style chat window for your team.

  • Ollama standard
  • Open WebUI default
  • No third-party AI API required by default
  • Managed setup and updates
Explore Local AI →

AI Apps

Private document search, team chat, workflow automation, and creative image tools — all open-source and managed by us.

  • AnythingLLM or LibreChat
  • Flowise and n8n options
  • ComfyUI for creative GPU workflows
  • vLLM only as optional advanced layer
Compare AI Apps →

GPU Infrastructure

The right GPU server for running AI, generating images, and private automation — sized to what you actually need.

  • Dedicated setup
  • Storage and backup planning
  • Monitoring and maintenance
  • Benchmark before performance promises
Plan GPU Stack →

Open Source Hosting

Managed open-source hosting with CyberPanel, domains, SSL, DNS, and human support.

  • CyberPanel control panel
  • WordPress, Nextcloud, Matomo and more
  • No cPanel or CentOS claims
  • Built for long-term maintenance
View Hosting Options →

Domains

Domain registration, renewal, transfer guidance, and DNS support for open-source projects and teams.

  • Popular TLDs with USD pricing
  • Renewal notes shown clearly
  • Transfers reviewed by registry rules
  • DNS basics included
Check Domains →

Choose each app on purpose

Each app has a job. We won't pretend every tool is ready for every team.

Open WebUI

The private, ChatGPT-style chat window your team opens in the browser.

AnythingLLM

Chat with your own documents and build private knowledge spaces.

LibreChat

Team chat that can use several AI providers, when that extra effort is worth it.

Flowise

A drag-and-drop builder for document search (RAG) and AI agents, for power users.

n8n AI Starter Kit

A workflow-automation starter kit — a proof of concept we harden before real use.

ComfyUI

AI image creation on your GPU, with the models and tools set up for you.

Private Team RAG Starter: a measured path to team knowledge

Start with a bounded private RAG audit on RTX 4000 Ada class 20 GB hardware: your documents, fixed questions, source citations, and permission checks. Qwen3-Embedding models make this worth testing now, but your storage, document types, speed, permissions, and GPU health decide what fits.

Step 1

Embedding benchmark

We test Qwen3-Embedding 0.6B, 4B, or 8B against your document mix before committing to a larger knowledge rollout.

Step 2

Knowledge ingestion

We pick AnythingLLM, Flowise, or a lighter setup once we know how your documents are split up, how often they change, and your privacy rules.

Step 3

Team controls

User roles, audit expectations, backups, and update windows are scoped before production use, not bolted on after launch.

Our promise: we make no live-AI claim until the NVIDIA drivers, Ollama, and your chosen model all pass tests on your real server.

Choose the right entry path: text and source-backed RAG goes to the €431 Private RAG audit (up to 25 files, 200 pages, and 15 fixed questions). Scans, forms, and images go to the €431 Document Intake trial (up to 10 files, 50 pages, and one workflow).

€431 Document Intake Trial for scans, forms, and images

Choose this bounded path for up to 10 files or images, 50 pages, and one extraction workflow. We start with sample pages, expected fields, a baseline for reading text and tables (OCR), and a human-review plan — not a vague promise of full automation.

Step 1

Sample pack

Select invoices, forms, scanned PDFs, screenshots, and expected fields. Remove secrets before testing and define what counts as an extraction error.

Step 2

Parser and vision-model trial

Use Docling-style conversion and OCR/table baselines, then test Qwen3-VL or Qwen2.5-VL (AI models that read images) against the same pages.

Step 3

Review workflow

Decide which fields may be automated, which need approval, and where logs, source files, and extracted outputs may live.

Our promise: we make no live document-AI claim until the NVIDIA drivers, the software, and your chosen model all pass tests on your real server.

Coding-assistant test for private code

Qwen3-Coder 30B is worth trying for whole-codebase work. We keep it practical: we test how much code it can read, its speed, how it fits your editor, and its limits before any team rollout.

Step 1

Repository sample

Select representative private code, docs, and issue patterns. Secrets and production credentials stay out of the benchmark corpus.

Step 2

Model-fit trial

Test Qwen3-Coder 30B and lighter fallbacks against real tasks, not generic demo prompts, while measuring memory, context, and response quality.

Step 3

Team rollout scope

Define IDE or web UI access, update windows, audit expectations, and fallback paths before developers rely on the assistant.

Our promise: Ollama lists Qwen3-Coder 30B at 19 GB, but a 20 GB RTX 4000 Ada card still needs driver checks, a healthy Ollama, and a real test on your server before production use.

Not sure which AI app fits? Ask first.

Goes to sales@ezoshosting.com. Reply by email.