Use n8n if you need a general-purpose automation platform with 400+ integrations that can also call LLMs. Use Flowise if you're building dedicated RAG chatbots or LLM agent chains with a visual drag-and-drop builder. They solve different problems — many teams run both.
| Feature | n8n | Flowise |
|---|---|---|
| Primary Use Case | Workflow automation + AI | LLM chain/RAG builder |
| GitHub Stars | 42,000+ | 28,000+ |
| Integrations | 400+ | ~80 (LLM-focused) |
| Visual Builder | ✓ Node-based flow | ✓ Drag-drop chatflow |
| LLM Support | OpenAI, Anthropic, Gemini nodes | 20+ LLM providers native |
| RAG (Vector DB) | Via Pinecone/Qdrant nodes | Native (6+ vector stores) |
| Self-Host RAM | 2GB+ recommended | 512MB minimum |
| Docker Image Size | ~850MB | ~420MB |
| Cloud Pricing | €20/mo (Starter) | $35/mo (Pro) — or free self-host |
| API Endpoints | ✓ Webhook triggers | ✓ Chat API only |
| Credential Management | ✓ Encrypted vault | ✓ Basic env vars |
| Multi-user / Teams | ✓ RBAC | ✗ Single user |
| License | Fair-code (Sustainable Use) | Apache 2.0 (fully open) |
Both tools are free to self-host. The real cost is infrastructure. Here's what you'll actually pay:
Skip the DevOps entirely. Deploy n8n and Flowise with one click, persistent storage included, auto-SSL, and subdomain routing — starting at $9.99/month.
You need to connect AI to existing business tools. Example: "When a new email arrives in Gmail, extract the intent with GPT-4, create a Jira ticket, notify Slack, and update the CRM." n8n excels at this because it has native nodes for 400+ services and handles branching logic, error handling, and retries.
You're building a conversational AI product. Example: "I want a customer support chatbot that searches our documentation (RAG), maintains conversation history, and escalates to a human when confidence is low." Flowise's drag-and-drop chatflow builder makes this a 15-minute setup instead of a 3-day code sprint.
You need production-grade AI automation. Example: n8n handles the orchestration layer (triggers, routing, notifications) and calls Flowise via its API for the actual LLM reasoning. This is the most common production pattern for teams running both.
Based on analysis of 500+ community discussions across GitHub Issues, Reddit r/selfhosted, and Discord servers:
| Sentiment | n8n | Flowise |
|---|---|---|
| Most praised | "Replaced 3 Zapier workflows for free" | "Got a RAG chatbot running in 10 minutes" |
| Most complained about | "Memory usage spikes on large workflows" | "No multi-user support for teams" |
| Breaking change frequency | Low (stable API) | Medium (fast-moving project) |
| Documentation quality | Excellent (paid team) | Good (community-driven) |
Persistent storage. Auto-SSL. Subdomain routing. No DevOps required.
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