Organizations with strict data residency requirements, air-gapped environments, or regulatory mandates that prohibit data leaving controlled infrastructure need a complete AI automation stack deployable entirely on private or on-premises servers. Every component in this stack can run without any external SaaS dependencies.
The pipeline
STEP 1Nn8n / Dify / Flowise / LangFlowDeploy full stack using Docker Compose on private server or on-premises VM
STEP 2N
n8n (self-hosted)
Configure n8n workflows connecting internal systems (no external SaaS calls)
STEP 3OOllama + DifyDeploy local LLM via Ollama for AI tasks without external API calls — Local LLM model processes data on-device with no network egress
STEP 4FFlowise (self-hosted)Build RAG pipeline over internal documents using Flowise with local vector store — AI retrieves from local vector store and generates answers using on-premises LLM
STEP 5LLangFlow (self-hosted)Prototype and test new LLM pipeline flows visually before production deployment
STEP 6Nn8n + DifyConnect n8n triggers to Dify AI agent endpoints via internal HTTP calls
N
STEP 1
n8n / Dify / Flowise / LangFlow
Deploy full stack using Docker Compose on private server or on-premises VM
N
STEP 2
n8n (self-hosted)
Configure n8n workflows connecting internal systems (no external SaaS calls)
O
STEP 3
Ollama + Dify
Deploy local LLM via Ollama for AI tasks without external API calls — Local LLM model processes data on-device with no network egress
F
STEP 4
Flowise (self-hosted)
Build RAG pipeline over internal documents using Flowise with local vector store — AI retrieves from local vector store and generates answers using on-premises LLM
L
STEP 5
LangFlow (self-hosted)
Prototype and test new LLM pipeline flows visually before production deployment
N
STEP 6
n8n + Dify
Connect n8n triggers to Dify AI agent endpoints via internal HTTP calls
Why this works
All tools deploy via Docker Compose on private servers. n8n handles workflow automation with all data staying on the private server. Dify and Flowise provide visual LLM pipeline building connected to local models via Ollama or an internal model endpoint. LangFlow provides an alternative visual environment for LLM chain prototyping before deployment.
Setup time
1–3 days (server setup, Docker deployment, and security hardening)
Difficulty
technical
Built for
Enterprise IT Architect, DevOps Engineer, CTO
FAQ
Which local LLM model works best for business automation tasks?
Llama 3.1 8B and Mistral 7B are strong choices for general business tasks running on CPU. For GPU-accelerated setups, Llama 3.1 70B provides near-GPT-4 quality for complex reasoning. For simple classification and extraction tasks, smaller models (Phi-3, Llama 3.2 3B) run faster and require less hardware.
Can n8n self-hosted connect to external SaaS tools while keeping data on-premises?
Yes. n8n can call external SaaS APIs (Slack, HubSpot, etc.) from your private server without storing data in any third-party automation platform. The key difference from SaaS n8n is that your workflow logic, credentials, and processed data all stay on your infrastructure.
Is there an enterprise support option for self-hosted n8n?
n8n offers an Enterprise self-hosted tier with SSO, advanced user management, audit logs, and paid support. This is the recommended option for production deployments in regulated environments that need SLA guarantees and security certifications beyond community support.