What Is Dify vs n8n?Dify vs n8n 比的是什么？
Dify is a visual builder for LLM applications: chat apps, agents and workflows in one Studio, with a dataset-native knowledge pipeline for RAG, prompt orchestration, and evaluation on datasets with scoring. n8n is a workflow automation platform with 400+ integrations: a trigger fires a node chain across Slack, HubSpot, PostgreSQL, HTTP APIs and more, and its AI Agent node plus LangChain nodes let a workflow reason with a model. The single-tool pages on this site are Dify and n8n. The core metaphor differs: Dify is an opinionated LLM app stack, n8n is a general automation engine that gained an agent node.Dify 是可视化 LLM 应用构建器：聊天应用、Agent 与工作流都在一个 Studio 里，知识库管线原生支持 RAG、提示编排、数据集评估打分。n8n 是工作流自动化平台、400+ 集成：一个触发器拉动 Slack、HubSpot、PostgreSQL、HTTP API 等节点链，它的 AI Agent 节点与 LangChain 节点让工作流也能调用模型推理。本站单品页见 Dify 与 n8n。核心隐喻不同：Dify 是观点鲜明的 LLM 应用栈，n8n 是通用自动化引擎、后来长出了 Agent 节点。
Pricing is where the confusion lives, so read the units, not the headline price. Dify Cloud meters message credits — roughly one model call each, so a workflow with several model calls burns several credits per answer — with Sandbox free at 200 credits, Professional at $59 a month (5,000 credits, 3 members) and Team at $159 a month (10,000 credits, 50 members); annual billing is $590 and $1,590. One source describes the Sandbox 200 credits as a total trial allowance rather than a monthly reset — a scope divergence this page flags rather than resolves. n8n Cloud meters workflow executions — one complete run, however many steps — with Starter at €20 a month billed annually (2,500 executions) and Pro at €50 (10,000); US visitors see the same tiers displayed in dollars, roughly $20–24 and $50–60 depending on billing choice, so currency and billing-toggle figures disagree across sources. Business, at about €667 a month billed annually (40,000 executions), is self-hosted only. On both platforms, LLM token costs are on top of the platform bill. Confirm the live table on each official site before buying.最容易踩坑的是价格：只看标题价一定错，要看计费单位。Dify 云端按 message credits 计费——约等于一次模型调用，一个含多次模型调用的工作流、一次回答要烧掉多个 credits——Sandbox 免费 200 credits，Professional $59/月（5000 credits、3 人）、Team $159/月（10000 credits、50 人），年付 $590 与 $1590。有一家来源把 Sandbox 的 200 credits 写成总额试用额度、而非每月重置——本页标注这一口径分歧、不擅自裁决。n8n 云端按 工作流执行次数 计费——一次完整运行算一次、不论多少步骤——Starter 年付约 €20/月（2500 次）、Pro €50（10000 次）；美国访客看到的同一档位以美元显示，约 $20–24 与 $50–60、随计费方式而异，各家来源的币种与档位数字并不一致。Business 约 €667/月年付（40000 次）仅限自托管。两边都要另付模型 token 费。下单前请到各自官网核对实时价目表。
Verdict: pick by deliverable. Shipping an AI product — a support chatbot over your docs, a RAG search app, an agent users talk to — pick Dify: its dataset-native RAG, prompt orchestration and evaluation are the product, not add-ons. Shipping automation — a lead routed, a ticket triaged, a report compiled across five systems — pick n8n: its 400+ integrations and per-execution pricing are built for that shape. Many real teams end up with both: n8n moves the data, Dify answers the questions. Self-host either when you already run infrastructure; buy cloud when you want someone else on call.
实在的结论：按交付物选。交付 AI 产品——基于自家文档的客服机器人、RAG 搜索应用、用户直接对话的 Agent——选 Dify：它的数据集原生 RAG、提示编排与评估就是产品本身、不是插件。交付自动化——线索分流、工单分诊、跨五个系统拼报表——选 n8n：它的 400+ 集成与按执行计费就是为这种形态生的。很多真实团队最后两边都要：n8n 搬数据，Dify 回答问题。已有运维能力就自托管，想有人值班就买云端。
— 中国吧 AI Tools Hub Editorial Team
Who Should Use It?谁适合用？
✓ Good Fit For适合以下场景
- Teams shipping an AI app, chatbot or RAG product要交付 AI 应用、聊天机器人、RAG 产品的团队
- Ops teams automating work across many SaaS tools要在多个 SaaS 之间做自动化的运营团队
- Self-hosters who already run Docker infrastructure已有 Docker 运维能力的自托管团队
✕ Not Ideal For不适合以下场景
- Teams expecting one tool to be best at both jobs指望一个工具两头都最强的团队
- Buyers comparing only headline monthly prices只比标题月价的采购方
- Non-technical users wanting zero-setup automation想要零配置自动化的非技术用户
Getting Started快速开始
1. Name the deliverable: AI product or cross-system automation?1. 先写清交付物：AI 产品，还是跨系统自动化？2. Self-host the matching one and run one real job end to end2. 自托管对应的一家、把一个真实任务跑通3. Price the cloud tier from your real volume, not the demo volume3. 按真实量给云端档位定价、别按演示量Key Features核心功能
- Dify: Dataset-Native RAGDify：数据集原生 RAG — Knowledge documents, chunking and retrieval are built into the app builder, with evaluation datasets and scoring.知识文档、分块与检索内建在应用构建器里，附带评估数据集与打分。
- n8n: 400+ Integrationsn8n：400+ 集成 — Trigger and action nodes across CRM, chat, databases and HTTP APIs, with code nodes when visual nodes run out.覆盖 CRM、聊天、数据库与 HTTP API 的触发与动作节点，画布不够时可用代码节点。
- Both: Agent Capabilities, Different Shapes两边都有 Agent、形态不同 — Dify agents live inside an LLM app; n8n AI Agent nodes orchestrate tools inside a workflow run.Dify 的 Agent 住在 LLM 应用里；n8n 的 AI Agent 节点在工作流运行中编排工具。
- Both: Free Self-Hosting两边都可免费自托管 — Dify Community is free on your infra (modified Apache 2.0 / Dify licence, no multi-tenant service); n8n Community is free with unlimited executions under the Sustainable Use Licence (fair-code, internal business use only — not OSI open source).Dify 社区版自托管免费（改良 Apache 2.0 / Dify 许可、禁多租户服务）；n8n 社区版免费且执行不限量（Sustainable Use 许可、公平代码、仅限内部业务使用——不是 OSI 开源）。
- Cloud Bills Meter Different Things云端账单计的东西不同 — Dify: message credits per model call. n8n: whole workflow executions. Model tokens are extra on both.Dify 按每次模型调用的 message credits 计费；n8n 按整次工作流执行计费；模型 token 两边另计。
Pros & Cons优缺点
✓ Pros优点
- Dify RAG, prompt orchestration and evaluation are first-class, not bolted onDify 的 RAG、提示编排与评估是一等公民、不是外挂
- n8n 400+ integrations cover the long tail of SaaS automationn8n 的 400+ 集成覆盖了 SaaS 自动化的长尾
- Both self-host free, so the platform bill can be zero两家都可免费自托管，平台费可以是零
- Per-execution vs per-message pricing lets you pick the cheaper meter for your shape按执行与按消息两种计价，让你按形态选更便宜的那把尺
✕ Cons缺点
- Headline prices are not comparable — units, currency and billing toggle all differ标题价不可直接比——单位、币种、计费方式全都不一样
- Dify multi-call workflows burn credits faster than a single answer suggestsDify 多调用工作流烧 credits 的速度比一次回答看起来快
- n8n high-volume cloud executions compound fast on lower tiersn8n 云端低档位的高频执行费用涨得很快
- Self-hosting trades the platform bill for your own ops time自托管是用自己的运维时间换平台费
Use Cases应用场景
Popular ways people use it.常见用法：
💬 Support Chatbot Over Your Docs基于自家文档的客服机器人
Dify: upload the docs, build the RAG app, ship the chat widget.Dify：上传文档、搭 RAG 应用、上线聊天组件。
🔔 Lead Routing Across Five Tools跨五个工具的线索分流
n8n: form trigger, enrich, score, route to CRM and notify in Slack — one execution per lead.n8n：表单触发、补全、打分、写入 CRM、Slack 通知——每条线索算一次执行。
📊 Nightly Report Compilation夜间报表拼装
n8n pulls five APIs on a schedule and delivers one report; Dify answers follow-up questions about it.n8n 定时拉五个 API 拼成一份报表；Dify 回答关于这份报表的追问。
🧪 RAG Quality Before Launch上线前的 RAG 质量验证
Dify evaluation datasets and scoring catch retrieval regressions before users do.Dify 评估数据集与打分在用户之前发现检索退化。
Similar AI Tools相似 AI 工具
Related single-tool pages on this site: Dify for the LLM app builder and n8n for the workflow engine. If you want a LangChain-native canvas instead, compare Flowise — already covered on this site.本站单品页：Dify（LLM 应用构建器）与 n8n（工作流引擎）。如果想要 LangChain 原生的画布，另比 Flowise——本站已有相关页。