I ran n8n Cloud for 6 months. $50/month for 5k executions, and my AI workflows were eating 80% of the quota on LLM calls. Then I found Dify — open-source, self-hosted, and built specifically for AI pipelines. Cancelled n8n, deployed Dify on a $6 VPS, and my monthly automation bill dropped to $6 total.
Why Dify Over n8n for AI Workflows
| n8n Cloud | Dify (self-hosted) | |
|---|---|---|
| Price | $50/mo (5k executions) | $0 (your VPS) |
| LLM nodes | Generic HTTP | Native (prompt, RAG, agent) |
| Vector DB | External | Built-in (Weaviate, Qdrant, PGVector) |
| Observability | Execution logs | Token usage, cost tracking, evals |
| Workflow DSL | JSON | YAML + visual canvas |
The 5-Minute Self-Host
curl -fsSL https://get.docker.com | sh
git clone https://github.com/langgenius/dify.git
cd dify/docker
cp .env.example .env
# edit .env: set SECRET_KEY, DB_PASSWORD, REDIS_PASSWORD
docker compose up -d
Visit http://your-vps:3000 and you're in.
My Actual Migration
I had 3 n8n workflows:
- RSS → summarize → Telegram (LLM-heavy)
- Webhook → classify → route to Slack/Email
- Cron → scrape → vectorize → search
In n8n, each LLM call was an HTTP node with manual JSON parsing. In Dify, workflow #1 became a single "Agent" node with a prompt template and a Telegram tool. Execution count dropped from 5k to 800/month because Dify's agent loops are native, not nested HTTP calls.
The Tradeoff
Dify is AI-first. If your workflow is "move data from A to B with 12 SaaS APIs," n8n's 400+ integrations win. If your workflow is "LLM does something with this data," Dify is objectively better — and free.
I prototyped the migration with MonkeyCode: https://ly.cyberserval.tech/iIETXiF
Anyone else running Dify in production? How's the stability compared to n8n's battle-tested queue?
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