Dify vs n8n

What they actually are

Dify is an LLM application platform. The core product is a builder for chat assistants, RAG apps, and agents, wrapped around model routing, a knowledge base, a prompt IDE, and a dataset annotation layer. The unit of work is an application that takes a user message and returns a generated reply, optionally calling tools or querying a knowledge base. Deployment is a hosted endpoint, an embedded chat widget, or a REST API your own product calls.

n8n is a general workflow automation tool that grew a strong set of AI nodes. The unit of work is a workflow triggered by a schedule, a webhook, or an event from one of around 400 native integrations, and the canvas is built around branching, looping, and passing JSON between nodes. The AI nodes — LLM models, vector stores, embeddings, the AI Agent node — live next to Salesforce, Postgres, Slack, and HTTP Request, and a workflow usually does work other than generating text.

The two products overlap whenever an AI step is involved, but the centre of gravity is different. Dify treats the model as the main act; n8n treats the model as one of many nodes the workflow happens to call.

RAG and knowledge bases

Dify ships a knowledge base as a first-class object. You upload PDFs, Word documents, Markdown files, or connect a Notion workspace; Dify handles chunking, embedding, and storage against a built-in vector index. Retrieval settings — chunk size, overlap, top-k, rerank model, score threshold — are exposed in the UI, and any application can attach a knowledge base as a context source without writing retrieval code. Most teams that pick Dify do so because the RAG layer works on day one.

n8n can do RAG, but it is a build-your-own job. An ingestion workflow reads a source, splits documents with the Text Splitter node, embeds them with an OpenAI or Cohere node, and writes the result into Pinecone, Qdrant, Supabase, or Weaviate. A second workflow takes a user query, embeds it, runs a vector search against the same store, stuffs the matches into a prompt, and calls the model. The pieces are all there and the docs include reference flows, but you assemble them and you maintain them.

When the product is the chat over the documents, the Dify path is shorter. When the RAG step is one of fifteen things a workflow does — pull a ticket, classify it, retrieve similar past tickets, draft a response — the n8n path keeps everything on one canvas.

Integrations

n8n has the catalog. The node library covers Slack, Notion, Airtable, HubSpot, Salesforce, Stripe, Shopify, Google Workspace, Microsoft 365, GitHub, Linear, Jira, and the long tail of databases and queues that real automation needs to touch. Authentication is handled at the credential layer, retries and error workflows are first-class, and the generic HTTP Request node closes the gap for any REST API without a dedicated node.

Dify's integration story is narrower because its job is narrower. Models are the deep integration — OpenAI, Anthropic, Google, Mistral, Cohere, AWS Bedrock, Azure OpenAI, and Ollama for local inference are all supported and switchable per application. Tools are exposed through a plugin system and a smaller set of built-in connectors (web search, code execution, a handful of SaaS APIs), and pushing a Dify output into Salesforce or pulling data from Postgres usually happens in the application calling Dify, not inside Dify.

A pattern that has emerged in teams running both: n8n owns the triggers and the integration glue, calls Dify over HTTP for the AI-heavy step, and writes the result back into the downstream system.

Self-hosting and license

Both products self-host cleanly. Dify ships a Docker Compose stack that brings up the API, worker, Postgres, Redis, and the Weaviate vector store in one command, with a Helm chart for Kubernetes. n8n ships a Docker image, a Compose stack, and a Helm chart of its own; SQLite covers small installs, Postgres covers production, and Redis enables queue mode for horizontal scaling.

The licenses are not identical. Dify is Apache 2.0 with an additional clause that restricts using Dify as the basis of a hosted multi-tenant product without a commercial agreement. n8n uses the Sustainable Use License, which is free for internal business use and modification but restricts running a competing hosted n8n service. For internal use and embedded use inside your own product, neither clause is visible in practice.

Pricing

Self-hosted, both products are free at the runtime layer and you pay for infrastructure and model tokens. Token costs are usually the largest line on a working AI workflow, and both products pass those through to whichever provider you configured.

On cloud, the meters differ. Dify Cloud has a free tier with a small monthly message allowance, then paid plans that scale by team seats, message volume, and knowledge-base size. The relevant number for most teams is monthly messages plus the embeddings cost when a knowledge base is being refreshed. n8n Cloud meters workflow executions: the Starter plan begins around $20 a month for 2,500 executions, the Pro plan around $50 a month for 10,000, and a workflow with twenty steps costs the same one execution credit as a workflow with two.

For an AI product where every user message is a model call, the Dify meter aligns with the work. For an automation that touches AI in one of ten steps, the n8n meter aligns better because the execution count does not balloon with workflow complexity.

Agent and workflow models

Dify supports several application types: a simple chat assistant, a chatflow with a visual graph, a workflow for non-conversational pipelines, and an agent that plans tool calls in a loop. The agent type is built around function-calling models, with a tools panel, a memory configuration, and an inspector that shows each reasoning step. The graph is shaped around model calls, and the runtime is tuned for streaming responses back to a chat surface.

n8n's AI Agent node sits inside a normal workflow. It accepts a system prompt, a list of tools, a memory store, and a model node, and returns once the model finishes calling tools. The surrounding workflow handles the trigger, persistence, and post-processing, so the agent shares the same retry and error semantics as every other node. For an agent that lives behind a Slack command and does five tool calls before responding, the n8n model is sufficient. For an agent that streams a chat response while consulting a knowledge base, Dify's runtime is the more natural fit.

Who should choose Dify

Dify is the right call when the AI surface is the product. Teams building an internal knowledge assistant, a customer-facing support bot, a documentation Q&A widget, or a vertical agent inside their own SaaS get the prompt IDE, the knowledge base, the model routing, and the chat runtime as one platform with one operational footprint. Switching from GPT-4o to Claude Sonnet to a self-hosted Llama variant is a dropdown change, and the dataset annotation tools support the evaluation loop that follows.

Who should choose n8n

n8n is the right call when AI is one ingredient inside a larger automation. Teams routing leads from a form into a CRM with an LLM-classified priority, deduplicating inbound tickets with an embedding similarity check, or summarising daily activity from a dozen SaaS tools benefit from keeping triggers, integrations, and AI nodes on the same canvas. The catalog, the Code node, the error workflows, and the credential system are mature in ways a pure AI platform does not need to be.