AI apps managed around open-source control

Pick the interface and workflow that matches the job: Open WebUI for local chat, AnythingLLM for knowledge spaces, LibreChat for multi-provider teams, Flowise and n8n for agents, ComfyUI for creative GPU work.

  • Managed Local AI with Ollama standard
  • Open-source hosting with CyberPanel
  • Domains, support, and direct offer paths
Order managed AI stack See GPU infrastructure

vLLM is optional for advanced/dev/performance scenarios and only positioned after benchmarks.

Managed Local AI

Managed Ollama-based local AI on customer-owned GPU infrastructure with Open WebUI as the default interface.

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

AI Apps

Private knowledge, team chat, workflow automation, and creative GPU app stacks managed around open-source tools.

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

GPU Infrastructure

Right-sized GPU servers for local inference, image workflows, and private automation stacks.

  • 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 the app layer deliberately

Each app has a role. We avoid pretending every tool is production-ready for every team.

Open WebUI

Default chat UI for Ollama-first deployments.

AnythingLLM

Private knowledge spaces and document chat workflows.

LibreChat

Multi-provider team chat when the extra operational footprint is justified.

Flowise

Visual RAG and agent builder for power users.

n8n AI Starter Kit

POC and workflow automation stack that needs production hardening before live use.

ComfyUI

Creative GPU workflows with curated node and model management.

Team RAG fast path for private knowledge

Qwen3-Embedding candidates make retrieval work worth testing now, but the operating promise stays measured: storage, document shape, latency, permissions, and GPU health decide fit.

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

AnythingLLM, Flowise, or a lighter retrieval layer is selected after chunking, source refresh, and privacy requirements are clear.

Step 3

Team controls

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

Runtime gate remains active: no live local-inference claim until NVIDIA driver visibility, Ollama health, and a target-model smoke test pass on the actual server.

Request Team RAG benchmark Review model-fit shortlist

Document intake benchmark for private forms and PDFs

Local document AI is attractive when the workflow starts with sample pages, expected fields, OCR/table baselines, and a human-review plan instead of a vague automation promise.

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 VLM trial

Use Docling-style conversion and OCR/table baselines, then test Qwen3-VL or Qwen2.5-VL candidates 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.

Runtime gate remains active: no live local document-intelligence claim until NVIDIA driver visibility, Ollama or framework health, and a target-model smoke test pass on the actual server.

Request document workflow trial Read benchmark checklist

Code assistant benchmark for private repositories

Qwen3-Coder 30B is worth evaluating for repository-scale coding work, but the offer stays practical: we benchmark context, latency, editor fit, and operational boundaries before a 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.

Runtime gate remains active: Ollama lists Qwen3-Coder 30B at 19 GB, but RTX 4000 Ada class 20 GB hardware still needs driver visibility, Ollama health, quantization/context checks, and a target-model smoke test before production use.

Request code assistant benchmark Review model-fit shortlist
Need the managed operating layer? Managed Local AI plans currently start from $299.18/mo for BYO server management, with final terms shown in checkout. Compare the plans before ordering →