How to Self-Host n8n & Flowise AI on a Dedicated Cloud VPS (Complete Guide)
Comprehensive walkthrough to deploy self-hosted n8n workflow automation alongside Flowise AI visual agents using Docker Compose, PostgreSQL pgvector, Redis Bull queues, and Caddy reverse proxy with automatic SSL.
Provision Recommended VPS Compute
Deploy a NodexVPS AMD EPYC Production Tier instance (c-eu-02 with 2-4 vCPU, 8GB DDR5 RAM, 40GB NVMe) to prevent container memory thrashing during heavy JSON parsing, browser automation, or vector embedding lookups.
Install Docker Engine & Docker Compose V2
Install official Docker packages and enable the systemd daemon to auto-start upon server boot.
Create Project Directory & Environment Vault
Initialize a secure directory for your compose definitions, persistent volume mounts, and secret environment credentials.
Deploy Full-Stack docker-compose.yml
Create the multi-container configuration hosting PostgreSQL with pgvector, Redis for queue persistence, n8n in queue mode, and Flowise AI visual canvas.
Configure Caddy Reverse Proxy & Automatic TLS
Provide secure HTTPS with automated Let's Encrypt certificates for both your n8n workflow interface and Flowise AI agent dashboard.
Connect Flowise LangChain Chatflows into n8n Webhook Nodes
Call your Flowise AI reasoning chains directly from within any n8n workflow using the HTTP Request node with sub-1ms internal docker network latency.
Performance & Concurrency Tuning for High-Burst Webhooks
Scale dedicated worker processes to handle over 10,000 requests/minute without slowing down the primary web user interface.
Table of Contents
- 1.1. Hardware Sizing & Recommended Tier
- 2.2. Install Docker Engine & Compose V2
- 3.3. Setup PostgreSQL (pgvector) & Redis
- 4.4. Deploy n8n & Flowise Stack
- 5.5. Configure Caddy Reverse Proxy & SSL
- 6.6. Connect Flowise LangChain Nodes to n8n
- 7.7. Production Webhook Concurrency Tuning
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