Overview and positioning
Dify markets cloud, community self-hosted, and enterprise private deployment paths for building LLM applications. The public pricing page currently highlights a Free cloud tier with a small message-credit allowance for trying models, paid team-oriented cloud packaging that expands message credits, apps, knowledge documents, and workspace collaboration, plus Enterprise private deployment and a Docker-friendly Community Edition for self-hosting. The product center of gravity is prompts, RAG knowledge, agents, and workflow graphs purpose-built around model calls rather than arbitrary SaaS glue.
n8n markets automation for technical teams with a canvas of nodes spanning business apps and AI. Official cloud pricing is execution-based rather than per-node-complexity: Starter is published around €20 per month billed annually for 2.5K workflow executions with unlimited users and a pool of AI credits, Pro around €50 per month billed annually for higher execution volume and collaboration features suited to small production teams, Business around €667 per month billed annually for larger execution quotas and company collaboration controls under a sub-100-employee framing, and Enterprise as custom. Self-hosting remains a first-class path under n8n’s license model. The center of gravity is reliable multi-step automation across systems, with AI as an increasingly important but not exclusive node class.
Core capabilities
Dify’s capability edge is LLM-native application structure. Teams get assistant/chatbot patterns, knowledge document ingestion quotas, workflow orchestration tuned for model steps, plugin/trigger styles aimed at AI product behavior, and packaging that separates prototype message credits from production workspace needs. Self-hosted Community Edition matters for teams that want data-plane control without abandoning the Dify interaction model. Enterprise private deployment addresses regulated buyers who will not put prompts and corpora on multi-tenant SaaS. The product assumes the artifact you ship is an AI application or internal AI workspace, not a general iPaaS.
n8n’s capability edge is integration breadth and automation semantics. A single workflow can mix CRM, billing, HTTP, queues, and AI nodes with execution history, streaming options, and data tables depending on plan. AI Assistant credits on cloud plans acknowledge model help inside the builder, but the economic unit remains workflow executions. That makes n8n excel when the hard problem is connecting many systems with branching logic, human-in-the-loop steps, and operational visibility. It is less opinionated than Dify about RAG corpora, prompt versioning, and assistant UX, which teams often assemble from nodes and external services instead.
Developer experience and workflow
Dify DX favors AI product builders who think in apps, knowledge bases, and model providers. Cloud free credits lower the cost of trying OpenAI/Anthropic/Gemini-class models inside Dify’s UI, while self-hosting appeals to teams that already run Docker stacks for AI services. Collaboration features on paid workspaces matter once multiple builders share prompts and apps. The trade-off is that non-AI business automation may feel second-class: you can call HTTP endpoints, but you will not match n8n’s catalog of polished SaaS connectors.
n8n DX favors technical operators and full-stack engineers who already automate business processes. The editor, execution logs, and fair-code self-host path are mature. AI nodes plug into that same discipline rather than inventing a separate AI console. The trade-off is LLM-app productization: building a polished multi-tenant AI assistant with managed knowledge quotas and end-user chat UX usually requires more assembly than Dify’s AI-app primitives. Teams doing both often run n8n for ops automation and Dify (or similar) for user-facing AI apps.
Pricing and procurement
Dify procurement mixes SaaS message credits and app/workspace limits with a sharp self-host/enterprise split. Free cloud is explicitly for trying core features with limited message credits; paid tiers expand monthly credits, apps, knowledge document quotas, and team seats; Enterprise is the private-deployment conversation. Buyers should model model-provider spend separately because message credits are not a full substitute for production token bills on customer-owned keys.
n8n procurement is execution-metered on cloud and license/ops-metered when self-hosted. Published annualized cloud rungs (~€20 Starter / ~€50 Pro / ~€667 Business) make growth scenarios legible if you can estimate monthly workflow runs. AI credits on plans are secondary to execution counts. Comparing Dify message credits to n8n executions directly is a category error; finance models must pick the primary workload metric first. Self-hosting changes both products’ TCO toward infrastructure and people cost rather than SaaS invoices.
Ideal use cases and trade-offs
Choose Dify when the roadmap item is an LLM assistant, internal AI workspace, knowledge-grounded support agent, or multi-step agent workflow where models and corpora are the product. It serves as the more practical daily standard for AI-first teams that want an application layer above raw orchestration. The trade-off is thinner general automation coverage versus n8n when the same team must also sync Salesforce, billing, and paging systems.
Choose n8n when the roadmap item is cross-system automation that sometimes calls LLMs, when connector breadth and execution observability dominate, or when the organization already standardizes on n8n for ops. It serves as the more practical daily standard for automation platforms adding AI nodes rather than AI apps adding a few webhooks. The trade-off is more assembly work for polished LLM product UX and knowledge management.
Verdict
Dify is the top recommendation for the buyer intent that dominates “Dify vs n8n” AI-app SERPs: teams trying to ship LLM applications and agent workflows with knowledge and model routing as first-class concepts. n8n remains the stronger general automation fabric and may still be the correct platform when AI is only one node among many enterprise integrations. For the LLM application platform decision, our pick is Dify.