Self-hosted AI keeps prompts and weights on your LAN for compliance. Run open models on GPU and point Apify webhooks at private inference APIs.
Self-hosted AI keeps prompts and weights on your own network for privacy and compliance. These guides cover running open models on a GPU and pointing Apify webhooks at private inference APIs.
Owning the inference stack means data residency and lower per-token cost at volume. Apify supplies the web data those private models process. Below you will find tutorials for self-hosted AI setups.
Dify and Flowise both let you build LLM-powered applications through a visual interface without writing a full backend. But they are aimed at different maturity stages: Flowise for rapid experimentation, Dify for production-grade deployments where teams need RAG knowledge bases, API publishing, and multi-user workspaces.
Ollama and vLLM both let you run open-weight LLMs on your own hardware without sending prompts to OpenAI or Anthropic. But they are built for different points on the scale curve — Ollama for developers and small teams, vLLM for production multi-user inference workloads where throughput matters.
OpenClaw and Hermes Agent are both self-hosted AI assistants you run on your own hardware — but they solve different problems. OpenClaw is a gateway that routes messages to an LLM and executes skills on demand. Hermes Agent is an autonomous agent with persistent memory and a learning loop that improves on repeated workflows. This post compares the two directly so you can choose the right tool for your setup.
ChatGPT Team costs $25 per user per month. At 10 users that is $3,000 per year — before you factor in rate limits, data residency concerns, and the fact that every prompt you send trains future OpenAI models unless you opt out. Self-hosting an LLM stack eliminates per-seat costs, keeps prompts on hardware you control, and lets you run models tuned for your domain.
TL;DR
docker-compose.yml: n8n + LiteLLM + Twenty CRM + shared PostgreSQL + Redis + CaddyMost sales teams that want AI-augmented automation end up in one of two bad places: paying for Zapier Pro, a direct OpenAI API key they can't audit, and HubSpot CRM — three separate subscriptions that don't compose well and bill regardless of usage. Or they write fragile Python scripts that break on every API change and nobody wants to maintain.
This guide deploys the middle path: Twenty CRM for pipeline management, n8n for visual workflow automation, and LiteLLM as an OpenAI-compatible AI proxy — all on a single Liquid Web 8 GB VPS. n8n calls LiteLLM for every AI task (lead enrichment summaries, proposal draft generation, inbound form classification), and LiteLLM provides budget caps, model fallbacks, and a unified request log.
TL;DR
docker-compose.yml: Ollama + LiteLLM + Open WebUI + Authentik + PostgreSQL + Redis + CaddyEvery prompt your team sends to ChatGPT Team or Copilot for Microsoft 365 leaves your network and is processed on OpenAI's or Microsoft's servers. For most teams that's an acceptable trade-off. For teams handling legal documents, source code, financial data, or customer PII, it is not. This guide deploys a private ChatGPT equivalent — Llama 3 70B running on your own GPU hardware, with a ChatGPT-quality web interface, your company's SSO, and per-team usage budgets — entirely inside your infrastructure.
TL;DR
docker-compose.yml: Dify (API + worker + web + sandbox) + Qdrant + Ollama + PostgreSQL + Redis + CaddyRetrieval-Augmented Generation (RAG) lets you ask questions against a private document corpus and get answers grounded in the actual content. The dominant hosted pattern — OpenAI Assistants + Pinecone — works, but every document chunk and every query travels to OpenAI's servers. For legal contracts, internal knowledge bases, or any non-public data, that is a compliance liability.
This guide deploys a fully self-hosted RAG stack: Dify as the orchestration and document management layer, Qdrant as the vector database, and Ollama serving both the embedding model and the chat LLM — all on a single Liquid Web VPS.
Cloud API costs scale linearly with usage. Self-hosting Ollama + n8n + Coolify on a $20/month VPS removes per-token fees for local Ollama inference — you still pay for the VPS, bandwidth, and your time; throughput is limited by CPU/RAM. When workflows call Apify, email, or other SaaS APIs, data transits those providers — not only your server.
This guide covers the complete setup from bare VPS to production-ready AI pipeline, with official documentation links for every step.
TL;DR:
| Component | Role | Official docs |
|---|---|---|
| Coolify | Infrastructure manager (replaces Heroku/Vercel for self-hosting) | coolify.io/docs |
| Ollama | Local LLM inference (Llama 3.1, Mistral, DeepSeek) | ollama.com |
| n8n | Workflow orchestration (visual automation) | docs.n8n.io |
| Qdrant | Vector database for AI memory/RAG (Retrieval-Augmented Generation) | qdrant.tech/documentation |
| PostgreSQL | Structured data storage | postgresql.org |
Prerequisites:
This guide takes you from a blank server to a fully running OpenClaw instance — accessible over HTTPS, connected to Telegram, with persistent storage and daily backups. Expect about 20 minutes start to finish.
OpenClaw is an open-source personal AI assistant you run on your own hardware. It connects to the messaging apps your team already uses — WhatsApp, Telegram, Slack, Discord, Signal — and can browse the web, execute tasks, and run on your choice of AI model. As of March 2026, it has 323,000+ GitHub stars, making it one of the most adopted self-hosted AI projects ever built.
The most commonly deployed self-hosted AI tools: n8n for AI workflow automation, OpenWebUI for a private ChatGPT-like interface (running against local models or the OpenAI API), ComfyUI for image generation, Flowise for visual LLM pipelines, and Ollama for running open-source models locally. Each serves a different use case — most teams start with n8n or OpenWebUI and add tools as needs grow.
Three main reasons: data privacy (your prompts and data stay on your server), cost at scale (n8n self-hosted saves thousands per month vs. cloud plans at high operation volume), and control (you run the version you want, keep your data, and are not subject to pricing changes or feature removal). The tradeoff is setup effort and maintenance responsibility.
Only for local LLM inference and image generation. n8n, Flowise, and OpenWebUI (when using the OpenAI or Anthropic API instead of local models) run fine on a standard CPU VPS for $5–$20/month. ComfyUI and Ollama running large models need a GPU — at minimum an NVIDIA GPU with 8GB+ VRAM. GPU VPS pricing starts around $0.50/hour for A4000-class hardware.
The fastest path is a deployment service. Describe what you want running (e.g. n8n + OpenWebUI on a Hetzner VPS), share server access, and receive a fully configured, documented stack in 1–3 business days. Services on this site start at $149 for a single tool deployment and $549 for a full AI stack bundle, with a written runbook so you can maintain things independently after handoff.
