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Vector Databases · kossakovsky

n8n-install

n8n-install is a Docker Compose template that deploys 30+ open-source tools (n8n workflow automation, Ollama local LLMs, Flowise AI agents, Qdrant vector DB, and others) with a single interactive command. It includes automatic HTTPS via Caddy, monitoring with Grafana/Prometheus, and pre-configured integrations—marketed as a self-hosted alternative to Zapier/Make.

Source: GitHub — github.com/kossakovsky/n8n-install
895
GitHub stars
228
Forks
Shell
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorykossakovsky/n8n-install
Ownerkossakovsky
Primary languageShell
LicenseApache-2.0 — OSI-approved
Stars895
Forks228
Open issues0
Latest releasev1.6.0 (2026-07-01)
Last updated2026-07-01
Sourcehttps://github.com/kossakovsky/n8n-install

What n8n-install is

Shell-based installer generating a Docker Compose stack with n8n in queue mode (Redis + PostgreSQL), optional AI/RAG services (Flowise, Dify, LightRAG, RAGFlow), vector databases (Qdrant, Weaviate), and infrastructure (Caddy reverse proxy, Prometheus, Grafana). Targets Ubuntu 24.04 LTS; requires Docker and manual post-deployment configuration for external access.

Quickstart

Get the n8n-install source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/kossakovsky/n8n-install.gitcd n8n-install# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Private AI Automation Lab

Organizations wanting full data sovereignty and no vendor lock-in can deploy a complete workflow + LLM stack on-premises. Useful for companies processing sensitive data or those needing custom AI integrations without cloud dependency.

Low-Code Workflow & AI Agent Development

Teams building internal automation, chatbots, and RAG systems can leverage n8n's 400+ integrations and Flowise/Dify's visual builders without coding. Accelerates time-to-value for process automation and AI-powered assistants.

Cost-Conscious Automation at Scale

High-volume automation workloads (thousands of monthly tasks) benefit from self-hosted n8n in queue mode with dynamic worker scaling, avoiding per-execution SaaS pricing while maintaining enterprise features (monitoring, redundancy).

Implementation considerations

  • Interactive installer handles secret generation and basic configuration, but production deployments require manual review of Caddy DNS, reverse proxy rules, and external service authentication (e.g., OpenAI/Anthropic keys for LLM fallback).
  • n8n queue mode with dynamic workers demands tuning: Redis memory, PostgreSQL connections, and worker count must align with expected throughput. No documented performance baselines provided; testing is mandatory.
  • Optional services (Flowise, Dify, LightRAG, RAGFlow) multiply memory/CPU overhead; infrastructure sizing must account for all selected components. Full stack on a single server may require 8+ GB RAM and sustained monitoring.
  • Automatic HTTPS via Caddy requires public DNS and internet connectivity. Internal-only deployments need manual certificate handling or self-signed setup; reverse proxy configuration is non-trivial.
  • No pre-built backup or disaster recovery orchestration. Users must implement PostgreSQL/Redis backup, volume snapshots, and state recovery—critical for production workflows.

When to avoid it — and what to weigh

  • No On-Premises Infrastructure — Teams without managed servers, Kubernetes, or DevOps capability should avoid. This requires Docker proficiency, ongoing container management, SSL cert renewal oversight, and backup strategy—unsuitable for minimal-ops teams.
  • Compliance Requires Vendor-Managed SaaS — If regulations mandate SaaS uptime guarantees, vendor SLAs, or third-party audit trails, self-hosted is counterproductive. Cloud-native alternatives (Zapier, Make) offer compliance certifications this setup cannot provide.
  • Rapid Iteration with Minimal Maintenance — Startups prioritizing fast feature deployment over infrastructure control should use managed platforms. This stack introduces overhead: security updates, container patching, disk management, and potential version compatibility issues across 30+ services.
  • Unmapped or Frequently Changing Integrations — If workflows depend on proprietary or newly-released SaaS APIs, n8n's 400+ integrations may lag behind. Community-maintained integrations carry deprecation risk; managed platforms often update faster.

License & commercial use

Licensed under Apache License 2.0 (permissive OSI-approved license). The installer itself is open-source; bundled components carry their own licenses (n8n: fair-code/community edition, Flowise: Apache 2.0, Ollama: MIT, Qdrant: AGPL/elastic, Supabase: Apache 2.0 + AGPL, etc.). Review component-specific licenses for commercial deployment.

Apache 2.0 permits commercial use, modification, and distribution of the installer itself. However, bundled components have stricter terms: n8n community edition has usage tiers; Qdrant uses AGPL (network copyleft); Supabase carries dual licensing. For commercial workflows, verify each service's commercial license terms independently. Requires review before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityNeeds review
Deployment complexityHigh
DEV.co fitGood
Assessment confidenceHigh
Security considerations

Infrastructure exposes multiple services via Caddy reverse proxy; assumes TLS termination works correctly but provides no audit of certificate pinning, CSP headers, or rate limiting. Bundled services (Ollama, Supabase, Neo4j) include networked APIs; default credentials and authentication strength depend on user configuration during setup. No mention of secrets management (Vault integration, encrypted env files). External API keys (OpenAI, etc.) stored in plaintext in compose/env files. Container image scanning, network segmentation, and secrets rotation are user's responsibility.

Alternatives to consider

Zapier / Make (Cloud-Native SaaS)

Fully managed, vendor-backed workflow automation with no infrastructure overhead, native compliance (SOC 2, HIPAA), and 1000+ integrations. Trade-off: per-execution pricing, vendor lock-in, data residency constraints.

Apache Airflow (Orchestration-First)

Open-source DAG-based workflow engine for data pipelines; preferred for scheduled batch jobs and complex dependencies. Trade-off: steeper learning curve, less visual builder, fewer out-of-box integrations than n8n.

Dify Standalone or Langchain Framework

Lightweight, code-first AI development stacks for custom LLM pipelines and RAG. Trade-off: no visual workflow builder, requires coding, no embedded n8n automation layer.

Software development agency

Build on n8n-install with DEV.co software developers

n8n-install provides the template; Devco can help architect, deploy, and operationalize it for your team. From infrastructure sizing to security hardening and ongoing maintenance, let's build your private AI platform responsibly.

Talk to DEV.co

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n8n-install FAQ

Does this run n8n in production mode?
Partially. n8n runs in queue mode (scalable workers + Redis) by default, which supports higher concurrency than simple mode. However, no SLA, load balancer, or failover is configured out-of-the-box. Multi-node deployment and disaster recovery require manual setup.
Can I use this for my SaaS product?
Yes, Apache 2.0 permits it, but verify component licensing: n8n community may have usage caps, Qdrant is AGPL (network copyleft—your product API must be open-source if you modify Qdrant), and Supabase is dual-licensed. Consult legal counsel.
What's the learning curve for n8n & Flowise?
Low for simple workflows (drag-drop UI), moderate for complex logic or custom integrations. Both have active communities but official documentation is sparse on advanced patterns. Budget 1-2 weeks for team onboarding.
How much infrastructure do I need?
Minimum: 2 CPU, 4 GB RAM, 20 GB disk (single-node). Typical deployment with Ollama + Flowise + RAG services: 4-8 CPU, 16-32 GB RAM, 100+ GB disk. Full stack with all services: dedicated server or VPS with ≥16 GB RAM recommended.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like n8n-install. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.

Ready to Self-Host Your AI Automation Stack?

n8n-install provides the template; Devco can help architect, deploy, and operationalize it for your team. From infrastructure sizing to security hardening and ongoing maintenance, let's build your private AI platform responsibly.