Dify vs n8n

For Dify vs n8n, pick Dify when the deliverable is an AI app that answers from your documents; pick n8n when it is a business process with AI inside it. Both support self-hosting, but running the software is only the beginning: Dify's published minimum is 4 GiB of RAM, before you size the deployment for your workload. Dify deployment requirements.

Dify vs n8n: Which Should You Pick?

Choose Dify for a document assistant, internal knowledge app or AI product whose main job is producing a useful answer. Dify is an application platform for large language models, the models that read and generate language. It brings knowledge management, prompts, models, conversational workflows and publishing into the same workspace.

Choose n8n for intake, routing, record updates and other business processes where AI handles part of the work. n8n is a workflow automation platform: you connect steps that move data between applications, apply rules and perform actions. Its AI Agent node can choose tools within that process.

For a support operator, the deciding question is concrete. Is the job to help someone find the right policy, or to receive a ticket, look up an account, draft an answer and update the support system? Start with Dify for the first. Start with n8n for the second. These are judgments about the work each platform organizes, based on Dify's documentation and n8n's Agent documentation.

Verified against both makers' live documentation, pricing pages and licence text on 27 October 2026. The criteria are document maintenance, application delivery, business actions, operating responsibility and commercial-use rights. This is a documentation-based comparison, not a performance benchmark or hands-on deployment test.

Prices above retain the currencies displayed on the makers' pages; no exchange-rate conversion is implied. The subscription units differ. Dify combines workspace capacity with model credits and other quotas; n8n meters workflow executions. A credit is not an execution, and neither is a complete measure of model usage. Choose the required capability first, then budget the selected architecture. The full n8n pricing guide covers its plan decision.

Document-Based AI Apps: Dify Wins

Dify is the stronger starting point when you need to keep an assistant's knowledge useful after launch. Retrieval-augmented generation, or RAG, means finding relevant passages from your own material and supplying them to the model with the question. The difficult work is often maintaining those passages, not drawing the chat box.

Dify gives you knowledge bases with document and chunk management, retrieval testing, metadata filters and retrieval settings. A chunk is a smaller passage indexed for search; metadata is a field, such as product or document type, that helps narrow the search. Those controls help you investigate why the assistant chose the wrong material. Dify knowledge documentation.

Consider a company publishing an assistant over installation manuals. A convincing answer from an obsolete manual is still wrong. The builder needs a repeatable way to inspect the source passages, replace the document and ask the failing question again. Dify puts those activities close to the application builder, which is why it wins this category.

The prompt and model console matters too. Dify's LLM node exposes prompts, model parameters, retrieval context and structured output settings. Its context handling can preserve citations from knowledge retrieval. Model-provider credentials are managed at workspace level, so you can change the model setup without rebuilding a separate application shell. LLM node, model providers.

The wall: a built-in knowledge base does not establish that an answer is correct or that a user should see a document. Treat retrieval quality and access rules as acceptance criteria. On Cloud, Professional's published capacity includes 3 members, 500 knowledge documents and 5GB of knowledge storage. A document-heavy assistant can hit a knowledge limit before it needs a more elaborate agent. Dify capacity limits.

n8n Can Do RAG and Provide Chat

n8n can ingest documents, split text, create embeddings and retrieve from vector stores. Embeddings turn content into numerical representations that support similarity search; a vector store keeps those representations available for retrieval. You connect these components into your workflow. n8n RAG documentation.

It also offers Hosted Chat and Embedded Chat through Chat Trigger, with authentication and memory options. You do not automatically need to build a separate frontend just because you chose n8n. Chat Trigger documentation.

The distinction is who will maintain the system. If your operator mostly fixes source material and answer quality, Dify is the more coherent starting workspace. If documents are one input among several systems already managed in n8n, adding retrieval there can avoid operating a second platform. Do not migrate a working n8n assistant just to obtain a feature it already has.

Business Workflows and Controlled Actions: n8n Wins

n8n is the stronger choice when success means that the right business record changed and the process can recover from failure. Its Agent node connects a model to tools, while the surrounding workflow handles the wider sequence. Error workflows provide a place to respond when an execution fails. n8n error handling.

For example, an intake workflow might receive an enquiry, extract the request, check an existing customer record and prepare a proposed update. Use explicit rules for required fields and duplicate handling. Use the model for interpreting the request. If an action needs judgment, send it for review before the write happens.

n8n supports human approval on individual AI tools: the workflow pauses, a reviewer approves or denies, and the tool runs only if approved. That is a useful control when an agent can send a message or modify a record. It does not mean every step should become an autonomous decision. Human review documentation.

The wall: your canvas still needs failure handling, permissions and a clear owner. n8n does not make a poorly defined business process reliable merely by giving it an AI node. The n8n agents vs workflows guide explains where fixed logic belongs; the n8n review goes deeper on the operating tradeoffs.

Dify Plugin Support Does Not Make It Chat-Only

Dify can call external services through tool plugins and imported OpenAPI definitions, a standard description of an API. It can also reuse a Workflow as a tool and connect to supported MCP servers, which expose tools through a common interface. Check the specific operation and authentication path your app needs, rather than choosing by an integration count. Dify tools documentation.

Dify also supports scheduled, integration and webhook triggers. A webhook is an incoming request that starts work when another system emits an event. The important boundary is the app type: triggers belong to Workflow, while Chatflow starts from a user message. Dify trigger documentation.

For an AI app that needs a few external actions, this may be enough. n8n wins when the surrounding business process becomes the main thing you need to inspect and maintain. An extra connector is not, by itself, a reason to move the whole application.

Agents: Choose by the Work Around the Model

Dify wins for an agent powering an AI application; n8n wins for an agent embedded in an operational process. An agent lets a model decide which available tools to use. A workflow specifies the sequence and conditions around that decision.

Dify distinguishes its Workflow and Chatflow builders from its newer Agent, which the docs currently label beta. That Agent can work in a sandbox, an isolated execution environment, running commands and working with files. It can be used as a chat application or as a workflow step. That is a different operating responsibility from simply calling a configured tool. Dify Agent documentation.

For a first document assistant, start with retrieval and a controlled response path. Add open-ended tool choice only when the task needs it. For an operations workflow, keep the business rules explicit and give the agent a bounded interpretation task. Neither platform's agent label establishes which model will answer your particular questions better.

If the harder decision is how to assemble the AI components themselves, the Langflow guide offers a useful adjacent comparison. For a wider shortlist, see the best no-code AI agent builders.

Self-Hosting Licences: Commercial Use Has Different Boundaries

Both can support commercial company work, but downloading the source does not grant every resale or embedding right. Read the licence against the product you intend to sell, including who gets a workspace and who controls workflow logic.

Dify: Workspaces and Frontend Branding Matter

Dify's own licence says: “Dify may be utilized commercially”. It is a modified Apache License 2.0, permitting commercial backend services and enterprise app development subject to additional conditions. Dify licence.

Operating a multi-tenant environment requires Dify's written authorization, and the licence defines a tenant as a workspace. Removing or changing its logo or copyright information in Dify's frontend also requires commercial authorization. The frontend restriction does not apply when you do not use that frontend.

For a company building an internal assistant, those terms differ from an agency selling separate customer workspaces. They also distinguish using Dify behind your own application from rebranding its supplied frontend. Do not treat “commercial use allowed” as permission to resell a multi-workspace platform.

n8n: Who Can Build the Workflow Matters

The Sustainable Use License states: “You may use or modify the software only for your own internal business purposes or for non-commercial or personal use.” n8n licence text.

n8n describes this as source-available, fair-code licensing, rather than OSI open source. Use outside the licence requires a separate commercial agreement. n8n licence explanation.

Its current FAQ permits internal business automation, paid setup and maintenance services, and product backends where users trigger workflows you built. It explicitly allows customers to connect their own accounts when they cannot build or configure the workflow logic. n8n licence FAQ.

The boundary changes when customers can create or modify workflows. A custom interface, API or AI agent acting on their behalf does not remove that restriction. Hosting a customer workflow builder or white-labelling n8n needs a different agreement.

For agencies, delivering a maintained automation and selling access to an automation builder are different offers. Confirm the intended architecture against the maker's terms before committing to the latter.

Self-Hosting Operations: The Stack You Choose Determines the Work

Choose neither on the assumption that a free licence removes the operating bill. Someone must own updates, backups, secrets, monitoring and recovery. Self-hosting the orchestration software also does not establish where an external model provider processes requests.

Dify's Compose deployment includes the application, workers, database, Redis, vector storage, plugins and sandbox services. Its upgrade instructions tell you to compare environment settings and account for changed services. The maintenance unit is the complete application stack. Dify deployment guide.

n8n's current Compose guide includes the Assistant sandbox stack and specifies 4 GB RAM and 2 vCPUs for that setup. That is not a universal size for every n8n deployment. A comparison between a basic workflow runtime and a complete document-and-agent stack would not establish which production system is cheaper to operate. n8n Compose guide.

There is also a collaboration boundary. n8n Community excludes projects, workflow and credential sharing, SSO and Git version control. Queue mode is included, so the wall is not simply whether you can run more workers. A company can outgrow the free edition's working arrangements while the server still has spare capacity. n8n edition comparison.

For an operator without an infrastructure owner, start the evaluation on managed hosting. For a company with a concrete deployment requirement, self-host the platform that fits the job and budget the complete system around it.

Using Dify and n8n Together

Combine them when knowledge maintenance and business automation have become substantial, separate jobs. Dify can serve an application through its API, while n8n can make API requests as part of a workflow. That supports a useful division of responsibility, but it also creates another boundary to monitor. Dify publishing options, n8n platform capabilities.

A proposed support architecture is: n8n receives a ticket, sends the relevant request to Dify, receives a document-grounded draft, routes it for review, then updates the support system. Dify owns retrieval and drafting. n8n owns the event, review and external write. This is an architectural recommendation, not a deployment result.

Define the handoff before building it: what information goes in, what fields come back, what happens when retrieval finds nothing, and which component may retry. A repeated request must not create a repeated external action. Keep the write in one place, with an identifier that lets you recognize work already completed.

Skip the combined setup if one platform already meets the acceptance criteria. Running both adds deployment, credentials, logs and failure cases. It earns its place when it reduces the complexity each owner must manage.

Switching From Dify to n8n, or n8n to Dify

Migration is a rebuild of behavior, not just an export. Dify exports its app definition as YAML DSL, a file describing the app configuration. n8n exports workflows as JSON. Those formats help move work within each platform; they do not establish cross-platform compatibility.

Dify's export includes prompts, model parameters, node settings and knowledge-base connections. It excludes the actual knowledge content, third-party tool API keys and usage logs. Secret environment variables can be included by choice, so inspect the export before sharing it. Dify app management.

n8n's exported workflow can contain credential names and IDs, and HTTP Request nodes imported from cURL can contain authentication headers. Inspect the file and plan to reconnect the target environment deliberately. n8n export documentation.

Move toward Dify when keeping documents, prompts and the answer experience usable has become the main maintenance burden. Preserve the source documents and evaluation questions, recreate retrieval behavior, and decide whether existing business automations should remain where they are.

Move toward n8n when the AI result is useful but the surrounding handoffs, approvals and record updates dominate the work. Rebuild those steps explicitly. You can keep a functioning Dify app behind an API instead of rewriting its retrieval setup immediately.

Do not switch because one platform is fashionable or because a demonstration used fewer boxes. A working system with understood failure modes is worth keeping until the replacement solves a named constraint. Estimate the migration in rebuilt connections, reindexed data, changed user access and regression checks, not the size of the exported file.

Your First Evaluation: Prove the Job Before Expanding It

Pick a small, representative piece of work and define what a correct result looks like. The useful comparison is how confidently you can operate that result after something goes wrong.

1. 1

   For a document app, start in Dify

   Connect a model provider, create a knowledge base from representative documents and build a Chatflow with retrieval feeding the LLM response. Test an answer present in the source, an absent answer and an ambiguous question. Inspect the retrieved passages. Publish the app and use its Access Point link to review the user experience. These are documented Chatflow and publishing paths.

2. 2

   For a business process, start in n8n

   Use one incoming event and one intended business action. Keep required-field checks explicit, restrict the agent's tools, and add human review where an action needs judgment. Exercise a failed request and a repeated input before widening the workflow. The acceptance test is the final record and recovery path, not just a plausible model response.

3. 3

   Choose the system someone can own

   Have the future operator identify the source of a wrong answer, locate a failed run and explain how a change reaches users. If the difficult part is knowledge and conversation, favor Dify. If it is the business process around the model, favor n8n.

Frequently Asked Questions

Is n8n outdated now?

No. Its current documentation includes AI Agent nodes, RAG, hosted chat and human review for tools. Choose by the work you need to operate, rather than assuming a newer AI-first platform replaces workflow automation.

Is Dify free?

Dify has a free Cloud Sandbox and a free self-hosted Community edition. Hosting, model usage and maintenance can still cost money. Commercial self-hosting must also satisfy Dify's workspace and frontend conditions.

Who has developed n8n?

n8n was founded by Jan Oberhauser in Berlin in 2019. n8n press information.

What is better than n8n?

Dify is the better starting point for a maintained document-based AI app. For workflows spanning business systems, n8n remains the stronger choice between these two. There is no universal winner independent of the job.

What are the top 5 automation tools?

A useful shortlist to evaluate is n8n, Dify, Zapier, Make and Microsoft Power Automate, but it is not an objective ranking. Dify belongs when the main deliverable is an AI application; compare the others against the specific business process and deployment requirements.

Can Zapier replace n8n?

For an app-to-app process that Zapier supports, it can be a candidate. Check every required action and recovery path before switching; a working trigger alone does not prove the whole workflow is replaceable. Zapier Workflows.

How much does n8n cost per month?

The table above lists the current Starter and Pro monthly equivalents with an annual commitment. Those figures are not month-to-month quotes. Cloud plans depend on execution volume; Community is free to self-host, with infrastructure and connected services budgeted separately.

Is Microsoft Power Automate like n8n?

Yes, in the broad sense that both automate business processes. Power Automate also covers desktop automation. Compare the required applications and operating environment before treating either as a direct substitute. Microsoft Power Automate.

What does n8n replace?

n8n can replace repeatable manual handoffs and custom integration scripts when their logic can be expressed as workflows. It still needs the source systems, valid credentials and an owner for exceptions. Replacing the connection between systems does not replace the systems themselves.

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