Dify vs n8n: pricing, features and which to choose

Dify is an application platform from LangGenius, founded in March 2023 according to the company. Its visual workflow canvas connects language models with business systems and covers application building, deployment, monitoring, and knowledge retrieval. LangGenius said in March 2026 that Dify ran on more than 1.4 million machines, reached users in more than 175 countries and regions, and had more than 2,000 teams and 280 enterprises. The announcement also reported a $30 million Series Pre-A led by HSG, with GL Ventures, Alt-Alpha Capital, 5Y Capital, Mizuho Leaguer Investment, and NYX Ventures participating. These figures describe company-reported scale and funding. Dify’s central appeal is control over the application layer, model connections, deployment location, and operational interface.

A typical Dify project begins with an app type such as Chatflow, Workflow, Agent, or text-generation application. The builder connects prompt and LLM nodes, conditional logic, code, HTTP requests, tools, and human-input steps on a canvas. Knowledge bases ingest documents, create embeddings, retrieve passages, and can rerank results for RAG responses. Teams add a provider key, test runs in the visual interface, inspect logs, annotate answers, and publish a web app or call the application through a REST API. Plugins and marketplace integrations add providers and tools. The API documentation covers chat messages, workflow execution, file uploads, conversations, feedback, annotations, knowledge bases, and streaming events. This makes Dify useful for prototypes that need a defined path toward production deployment.

Dify’s main trade-off is operational choice: Cloud reduces administration, while self-hosting puts upgrades, security, storage, and provider credentials under the customer’s control. Community use must comply with the Dify Open Source License, whose source-available terms can restrict some commercial deployments. Langflow and Flowise are closer visual builders, while n8n focuses on broad automation and event integration. Dify fits teams needing a model-flexible LLM application layer, RAG, application APIs, and cloud or self-hosted deployment. Choose n8n when AI is one step inside a wider business process, or Langflow when a developer-oriented orchestration canvas is the priority. Pick Dify if your team needs to build and operate AI applications with visible workflows, knowledge bases, and provider choice.

n8n is a workflow automation company founded by Jan Oberhauser in 2019. It says its platform had more than 200,000 users and 3,000 enterprises by March 2025, and announced a $180 million Series C in October 2025, bringing total funding to $240 million and valuation to $2.5 billion. Its canvas combines code, integrations, credentials, webhooks, schedules, and AI nodes. Cloud hosting is the easiest entry point, while Community Edition is free to self-host. Pricing is based on workflow executions rather than steps or users. Self-hosting gives developers control over infrastructure and workflow data, but requires responsibility for security, backups, execution data, community nodes, telemetry settings, and license compliance. n8n is best for technical teams that value self-hosting, execution-based pricing, and AI model choice.

A typical n8n workflow begins with a schedule, webhook, polling event, or application trigger. Nodes can transform data, call APIs, run code, route conditions, query an AI model, or send work for human review. Each complete automatic run counts as one production execution, regardless of node count, while manual editor tests are excluded. Features include templates, Data Tables, sub-workflows, credential connections, retries, execution inspection, and AI Agent nodes. In 2025, n8n added Evaluations, allowing builders to test inputs, compare outputs, and apply measures for correctness, toxicity, bias, or tool choice. Users can connect multiple model providers according to cost and data needs. This workflow approach suits technical teams seeking execution-based pricing, self-hosting, and model choice.

n8n’s main tradeoff is operational and legal complexity. Self-hosting keeps workflow data on your infrastructure, but administrators must secure servers, manage backups, prune execution data, review community nodes, and configure telemetry settings. Its Sustainable Use License allows internal business use and consulting, but prohibits selling hosted n8n or exposing workflow building to customers without a commercial agreement. Zapier is easier for nontechnical users, Make offers a polished visual alternative, and Pipedream is stronger for code-first integrations. n8n’s execution-based billing can suit teams with many-node workflows, while its provider flexibility supports different cost and data requirements. Choose n8n when self-hosting, model choice, and infrastructure control matter more than a beginner-first interface, especially for technical teams and AI workflow builders.