TL;DR — n8n and Dify often show up together in self-hosted AI evaluations, but they want to own very different layers of your stack. After evaluating both against a custom self-hosted AI setup, we adopted n8n and skipped Dify. The decision came down to one question — “what slice does this want to own, and do I already own that slice?” — and the answer was opposite for the two platforms. This post lays out the framework so you can run the same evaluation on your own stack.

Why This Comparison Matters in 2026

Six months ago the question “which OSS AI platform should I run?” had maybe three serious answers. Today there are dozens, and they overlap aggressively. Dify and n8n keep showing up together in evaluation lists, partly because they’re both written in TypeScript, both self-hostable via Docker, both have visual editors, and both can talk to LLMs.

That surface similarity is misleading. They want to own entirely different layers of your stack. Treating them as alternatives is a category error that will cost you a week of deployment work and rework.

What we concluded after evaluating both against an existing self-hosted setup:

• Dify wants to be the orchestrator. If you already have one, Dify has nothing to offer.
• n8n wants to be the execution layer. If you don’t have one, n8n is one of the best off-the-shelf options available.

What Dify Is

Dify is an open-source LLM application development platform (Apache 2.0, 55k+ stars on GitHub). The pitch:

• Visual workflow editor — drag nodes to build AI pipelines
• Built-in RAG — upload docs, get a queryable knowledge base
• Agent builder — pre-packaged prompt templates with tool calling
• Model gateway — abstract over OpenAI / Anthropic / DeepSeek / local
• Observability dashboard — request logs, latency, cost

In 2025–2026, Dify replaced its underlying LangChain with a custom “Beehive Runtime,” which is impressive engineering. The product is genuinely well-built.

The target user: someone who wants to ship an AI app without writing code or maintaining infrastructure pieces individually.

Same family: Flowise, Langflow, FastGPT. These are all “platform-first” AI builders.

What n8n Is

n8n is open-source workflow automation (162k+ stars). Think Zapier, but self-hosted and with code escape hatches.

• 400+ SaaS connectors — Notion, Slack, Stripe, Telegram, GitHub, you name it
• Trigger → action → condition visual workflow editor
• Webhooks — receive external events, route to actions
• Polling triggers — RSS feeds, scheduled jobs, file watches
• Native retry/error handling — every node has retry policies

n8n is not trying to be an LLM platform. It has nodes that call LLMs, but its core identity is “connect arbitrary SaaS systems and react to events.”

This distinction is crucial. n8n is plumbing-first, with optional AI nodes. Dify is AI-first, with everything else folded in.

The Triage Question: Replace or Absorb?

When you evaluate any platform against an existing stack, the question is not “is it good?” The question is “what layer of my stack does this want to own, and do I already own that layer?”

There are exactly two outcomes:

• Replace: the platform wants to own a layer you already have. Adopting it means ripping out working code and replacing it with a less flexible black-box equivalent.
• Absorb: the platform wants to own a layer you don’t have yet. Adopting it fills a gap without competing with anything.

This frame turns what could have been a fuzzy multi-day debate into clean, fast decisions. The rest of this post applies it to each platform in turn.

Dify’s Footprint Across Your Stack

Dify wants to own five layers at once. Here’s how each maps against a setup that already has a code-based orchestrator (any agent harness — Claude Code, LangGraph, your own):

The deeper realization: Dify is built for people who don’t write code but want to ship an AI app. That’s a legitimate market, and Dify serves it well. But if you already have a code-based orchestrator running, adopting Dify means ripping out working pieces and replacing them with less flexible equivalents just to fit inside a visual UI. Net cost: a week of migration, all flexibility lost, zero new capability gained.

Our verdict: skip. Not because Dify is bad, but because there was no gap left for it to fill.

n8n’s Footprint Across Your Stack

n8n’s pitch is structurally different. It doesn’t want to be the brain. It wants to be the wiring.

The four core capabilities n8n offers, mapped against a typical custom AI setup:

The critical observation: none of these compete with what an existing orchestrator typically owns. They sit underneath. They fill gaps that a code-based orchestrator alone would still have:

• Event-driven entry points (most custom stacks only have cron)
• Pre-built SaaS adapters (most custom stacks have no generic adapter layer)
• Off-the-shelf retry semantics (most custom stacks have handwritten error handling)
• Public RSS polling for protocol-locked services like YouTube (most custom stacks have nothing)

Our verdict: absorb. n8n becomes a dependency — a well-maintained, well-documented, battle-tested execution layer — without competing with anything that already works.

The Architecture Pattern That Emerges

The mental model after these two decisions:

                       Orchestrator (decisions, judgment)
                       ─────────────────────────────────
                                   │
            ┌──────────────────────┼──────────────────────┐
            │                      │                      │
            ▼                      ▼                      ▼
       Knowledge layer        Tool interfaces        Time triggers
       (your RAG)              (your APIs /            (cron)
                                MCP servers)
                                   │
                                   ▼
                       ┌────────────────────────┐
                       │  n8n (execution layer) │
                       │  ───────────────────── │
                       │  • webhooks            │
                       │  • SaaS adapters       │
                       │  • RSS / polling       │
                       │  • retry / state       │
                       └────────────────────────┘

The hard rule worth setting yourself: n8n is execution only, never decision. No AI reasoning inside an n8n workflow. n8n receives signals, dispatches them, retries on failure, and reports back. All judgment stays in the orchestrator’s hands.

Why is this rule necessary? Because n8n has LLM nodes. You could put a “summarize this email” GPT call inside a workflow. The moment you do, you’ve split your reasoning across two places — some inside your orchestrator’s prompt context, some inside an opaque n8n node — and now you have two systems making decisions with no shared memory. That’s the failure mode that turns simple workflows into unmaintainable chains.

Keeping n8n as pure plumbing is the discipline that makes the architecture work.

Three Gotchas Worth Knowing Before You Deploy n8n

Three things that tend to surprise people in the first day of running n8n:

1. The REST API doesn’t support PATCH for archiving workflows. You can create and read workflows via the API, but you can’t delete or archive them programmatically. Cleanup has to go through the web UI. If you’re planning to dynamically generate workflows, factor in manual cleanup or write directly to the SQLite database. (Fixed in n8n 2.22+; the 2.21.x line still has this limitation.)

2. Webhook paths are globally unique, even for inactive workflows. Delete a workflow but the webhook path stays registered, blocking reuse in any new workflow. Treat the webhook namespace as a flat global you have to manage. Prefix paths with the workflow name from day one.

3. The API key scope doesn’t include workflow:execute. You can read workflows over the API but you can’t trigger them programmatically — webhooks are the only execution surface. For most architectures this is actually correct (webhooks ARE the integration point), but it can catch you off guard if you’re expecting “API to start a workflow run on demand.”

When You Should Choose Dify

To be fair: Dify is the right tool when:

• You don’t want to write code or maintain individual infrastructure pieces.
• You need a polished UI for non-technical users to build and tweak workflows.
• You want a one-stop hosted experience (RAG + model gateway + observability + UI) and don’t already have these pieces wired together.
• You’re building a customer-facing chatbot for a small team and need shipping speed over architectural flexibility.

If any of those describe you, Dify is a serious choice and we wouldn’t argue against it.

When You Should Choose n8n

n8n is the right tool when:

• You need to integrate with specific SaaS products (Notion, Slack, Stripe, Telegram, etc.) and don’t want to write each API client by hand.
• You want event-driven workflows (webhooks, polling, scheduling) without building your own event bus.
• You want a visual editor so non-technical teammates can see and modify pipelines.
• You’re OK with workflows being execution-only — no judgment, just plumbing.

n8n is not a good choice when:

• You need multi-step LLM reasoning with shared memory across steps. Use an agent harness instead (Claude Code, LangGraph, OpenAI’s Agents SDK).
• You need full control over prompt format, token budget, fallback chains. n8n’s LLM nodes are too abstract for serious work.
• Your workflow logic changes weekly. The visual editor is great for stable workflows; it’s a drag for rapidly iterating ones — code is faster to refactor than nodes.

The Deeper Principle: “Models Are Commodity, Orchestration Is the Moat”

The Dify-skip / n8n-absorb decision is downstream of a broader principle:

• Models (DeepSeek, GPT, Claude, Mistral, Llama) are interchangeable. Swap them with an env var.
• Platforms (Dify, LangFlow, Flowise) are also interchangeable. They package similar capabilities differently.
• Orchestration — the system that connects models, knowledge, tools, and outcomes — is where the leverage is.

When you already have a strong orchestrator, you do not need a platform that wants to be your orchestrator. You need plumbing that does plumbing well. That’s where n8n earns its place.

This principle generalizes. Every time you evaluate an AI platform, ask: does this want to own my orchestration layer, or fill a gap underneath it? If the answer is “own,” and you already have an orchestrator, skip it. If the answer is “fill a gap,” and the gap is real, absorb it.

Closing Thought

Two platforms. Opposite decisions. Same underlying logic: what slice does this want to own, and do I already own that slice?

• Dify wanted to own the orchestration layer → already covered → reject.
• n8n wanted to own the execution layer (event triggers, SaaS integration, retry, polling) → not covered → absorb.

If you’re evaluating self-hosted AI tools right now, this is the question to ask first. It saves a lot of pointless deployments and even more pointless rework.

References

• Dify on GitHub — 55k+ stars, Apache 2.0
• n8n on GitHub — 162k+ stars, Sustainable Use License
• Model Context Protocol (MCP) specification
• Our previous post: RAG vs Agents