A Thorough Comparison of Dify, n8n, and Make — Best Picks by Use Case, and Why n8n Often Gets Left Out of Comparisons

Key Points First

• Different positions: Dify is an AI platform (LLMOps) specialized for generative AI app/agent development. n8n and Make are general workflow automation tools (iPaaS/IPA) where AI is “one of many features.”
• Price ballpark: Dify offers paid cloud plans (e.g., Professional monthly) plus OSS/self-hosting. n8n has tiered cloud pricing (Starter/Pro, etc.) and self-hosting. Make uses a credit-based monthly subscription (credit ≒ number of operation runs).
• Strengths: Dify has a strong base for RAG/agents/observability/model switching. n8n excels at self-hosting + flexible branching/conditions, with a Sustainable Use License. Make provides highly visual no-code scenarios, loads of connectors, and credit-based cost control that’s easy to grasp.
• Why “n8n is missing” from many comparisons: (i) The comparison topic was AI-centric, so AI-native Dify and SaaS-first Make took priority; (ii) The selection criterion was “pure SaaS only,” excluding n8n’s self-hosting; (iii) Teams avoided the explanation cost of n8n’s Sustainable Use License and self-host billing nuances; (iv) In Japan-side ops, Make’s training assets often come first — all pragmatic design choices rather than product inferiority.

Who Will Find This Useful (Concrete Profiles)

• DX/automation owners at enterprises: Want a balance of ops cost and AI features across existing SaaS like Salesforce/Google Workspace/Slack.
• AI product teams: Need RAG/agents assembled with GUI + API, aiming for observability, audits, and model swapping on an LLMOps base.
• IT/IS departments: Have data residency or on-prem/self-host requirements and need a controlled governance × automation landing zone.
• SMB team leads: Want to start small, standardize, then adopt, with minimal UI learning and training cost.

This article is based on primary sources (official sites/docs), excluding uncertain info. Prices/features reflect Oct 2025 and may change with upstream updates; always recheck official pages.

1. Product “Philosophy” and Core Capabilities (Align the Frame First)

Dify (LangGenius)

• Philosophy: An AI platform that unifies agent workflows/RAG/model ops/observability needed for production AI apps. Supports OSS and cloud, and spans multiple model providers.
• Representative features: Agents, workflows, RAG, eval/observability, model switching, marketplace. Recent releases strengthen visual knowledge pipelines for enterprise data prep.
• Best for: FAQ/enterprise search bots, AI summarization/classification, and AI-infused business apps launched quickly.

n8n

• Philosophy: Self-hostable workflow automation. Build branching/conditions/loops flexibly and connect SaaS/APIs with low code. AI nodes are expanding. License is “Sustainable Use License.”
• Representative features: Node-based visual flows, self-hosting, webhooks/triggers, branching/filters/error handling, AI integration nodes.
• Best for: Internal SaaS glue (CRM ↔ spreadsheets ↔ mail), scheduled system sync, bridging on-prem and cloud.

Make (formerly Integromat)

• Philosophy: No-code SaaS automation via visual “scenarios.” Huge connector library, visual debugging, and credit (operation) pricing that’s intuitive. AI integration is GUI-first and easy to adopt.
• Representative features: Drag-and-drop scenarios, rich branching/filters/iteration, detailed logs, AI/webhooks/HTTP.
• Best for: SaaS-centric automation, bottom-up PoC→production, and deployments prioritizing low training overhead.

2. Pricing & Plans (Which One Is Easiest for First Steps?)

• Dify: Paid cloud plans (e.g., Professional/month) plus OSS/self-hosting. Model inference costs are separate, so think in two layers: platform fee + model usage.
• n8n: Cloud uses tiered, run-based pricing (Starter/Pro, etc.) and also supports self-hosting. Concurrency/history/ops controls scale by plan.
• Make: Credit-based (≒ module operation count). Free→paid tiers are broad. A November change adjusts certain credit conditions. The “pay for what you run” feel is clear, making PoC→production transitions easy.

Prices move with FX/campaigns/revisions. Safest path: estimate → run for a month → then contract.

3. What Each Does Best (From a Practitioner’s View)

3-1. Where Dify Shines (Turning AI Apps into “Platformized” Assets)

• RAG/agents, model switching (OpenAI/Anthropic/Vertex, etc.), and observability/evaluation are all GUI-driven — easy to productionize.
• Self-hosting/OSS helps satisfy data retention/residency.
• Examples: Internal policy Q&A, knowledge search, summarization/classification, agent-mediated automated processes.

3-2. Where n8n Shines (Soft, Powerful “Gluing”)

• Self-hosting centers on secure on-prem ↔ cloud glue with rich control (branch/filter/retry).
• The Sustainable Use License is fair-code. Once reviewed (e.g., resale terms), it becomes a controllable automation base.
• Examples: Core DB ↔ SaaS sync jobs, webhook-driven real-time links, audited automations.

3-3. Where Make Shines (Anyone Can Run It, Visibly)

• No-code visual scenarios are easy for business teams. Granular logs and GUI branching/iteration reduce training time. Connector breadth + credit model make cost planning simple.
• Examples: Slack notifications, spreadsheet roll-ups, CRM record updates — admin-led automations.

4. Security / Data Residency / Governance

• Dify: Self-hosting keeps RAG/model logs within your infra. Cloud edition offers DPA and governance docs.
• n8n: Self-hosting enables network-bounded designs and suits high-control departments. Up-front license comprehension and ops costing are required.
• Make: SaaS-first; handle residency/permissions via account design + least privilege. Credits also act as a guardrail against runaway loads.

5. Comparative Use Cases (From Small to Production)

1. Internal FAQ bot + document search

• Dify: RAG templates → model settings → eval/observability all via GUI. Fast to production.
• n8n/Make: Hand off Q&A to external LLM/API and flow the pre/post transforms and SaaS updates.

2. E-commerce: order notice → stock allocation → accounting entry

• Make: SaaS connector depth + GUI logs help frontline ops.
• n8n: Self-host for two-way with internal DB/ERP; branching freedom shines.
• Dify: Insert agents to handle exceptions with human escalation — hybrid autonomy.

3. Automated reporting (summaries/visuals)

• Dify: Package summarization/explanations as an app; swap models to tune cost/quality.
• Make/n8n: Excel at data collection/transform, then LLM summarize → deliver via Slack/email.

6. Pros & Cons at a Glance

Dify

• Pros: AI-native (RAG/agents/observability/model ops). OSS/self-hosting enables data control.
• Cons: For pure SaaS automation, connector breadth may lag iPaaS leaders. LLM cost optimization and eval ops design are key.

n8n

• Pros: Self-hosting × high flow freedom. Dev-friendly, strong for on-prem links.
• Cons: Requires understanding the Sustainable Use License. Cloud plan limits (runs/concurrency) need ops design.

Make

• Pros: Credit model that’s clear, excellent GUI logs, huge SaaS connector library — fast to first value.
• Cons: No self-hosting. For strict governance or direct on-prem links, design workarounds are needed.

7. Why “n8n Is Missing” from Some Comparisons (Common Background & Proper Framing)

1. The comparison topic skewed “AI platform.”
   Dify sits as LLMOps (AI-app operations base); Make is SaaS automation for the masses. n8n = general-purpose flows + self-hosting, which reads more technical — thus gets dropped from “AI-only” or “pure SaaS” tables.

2. The selection rule was “SaaS-only.”
   When security review / speed favors SaaS-only, self-host-first n8n is excluded. n8n Cloud exists, but many policies treat “self-host = extra review,” so editors skip it to lower explanation cost.

3. Avoiding license explanations (Sustainable Use License).
   Fair-code licenses require explaining how they differ from permissive OSS. For quick write-ups, tools with more caveats get omitted — that’s process economics, not a knock on n8n.

4. Ops design parameters add verbosity.
   Run counts/concurrency/history management needs an owner. For short articles, Make’s credits or Dify’s AI-app stance are easier to summarize.

Bottom line: n8n isn’t omitted because it’s worse, but because axes, page limits, and explanation cost sometimes exclude it.

8. How to Choose When You’re Unsure — Scenario-Based Picks

• You want to ship AI apps to production (with eval/observability/RAG tuning).
  → Dify. It platformizes models/observability/RAG, future-proofing model changes.

• You want low-code SaaS glue and have on-prem in the mix.
  → n8n. Self-hosting eases in-boundary ops; flexible branching/conditions pay off. Review the license early.

• You want business teams churning out working flows in days.
  → Make. No-code GUI, detailed logs, credit pricing make training & estimating easy.

9. Outlook Toward 2026

• Dify: Continued agent/workflow sophistication and knowledge-prep (e.g., visual pipelines), leaning into an “OS for AI apps.” Multi-model ops and observability remain growth areas.
• n8n: More AI nodes, cloud ops refinement, and enterprise features. Fundraising/growth stories suggest good product velocity.
• Make: Ongoing announcements around AI connectivity (provider flexibility) and credit optimization; sharpening the mass-market automation core. UI/log improvements and training content are key.

Side trend: The shift “from pure RAG to agentic patterns” keeps shaping AI-app design. Agent/workflow-oriented bases like Dify align well with this trajectory.

10. 90-Day Adoption Roadmap (Template)

• Day 1–14: Requirements

  • Data location (in/out), cross-border needs, audit level, AI goals (summarization/classification/RAG/agents).
  • Decide SaaS-only vs. include self-host (this is where n8n’s role gets set).

• Day 15–30: PoC

  • Dify: prototype FAQ/RAG. Make: 2–3 frontline flows. n8n: one on-prem ↔ SaaS bridge.
  • Score by cost (model + platform) and MTTR.

• Day 31–60: Standardize

  • Codify roles (AI app = Dify / SaaS automation = Make / boundary bridging = n8n).
  • Define log retention, permissions, audit, retry policy, versioning.

• Day 61–90: Production

  • Re-estimate SLA & ops cost, confirm audit trails, run training sessions (Make for business, n8n for IS, Dify for AI product).

11. FAQ

Q. Do we have to pick just one?
A. No. Use division of roles — AI apps = Dify, business-led GUI automation = Make, on-prem boundary glue = n8n. Overlap exists, but strengths are distinct.

Q. Concerned about n8n’s license.
A. The Sustainable Use License is fair-code. Have Legal review commercial/re-sale terms. If you need governed self-hosting, n8n remains strong.

Q. How to start small?
A. Make (free/low tiers) for visible quick wins → Dify (free/OSS) for AI-app prototypes → n8n to validate boundary/on-prem needs — a practical three-point approach.

12. Conclusion — Handle the “Missing n8n” Issue Correctly and Aim for a Three-Way Fit

• Dify is the operational base for AI apps. Strong at RAG/agents/observability for production design.
• n8n is the self-hosted, flexible bridge. AI-capable, but fundamentally general automation. When it’s missing from a chart, check whether the comparison was AI-only or SaaS-only.
• Make delivers “first runs” fast with no-code UI/logs/credits.

Takeaway: Rather than forcing a winner, let them co-exist by role: AI apps = Dify, speed for business = Make, boundary/governance = n8n. When you see “n8n not listed,” always verify the comparison assumptions. Change the premise, and the answer changes. The smoothest path is start small → define roles → standardize for friction-free automation.

References (checked: 2025-10-29)

• Dify

  • Official site (features/latest releases): mentions Agentic Workflow / RAG / model ops / observability.
  • Pricing page (cloud paid plans).
  • GitHub (OSS/self-hosting stance).
  • Official blog (Summer 2025 highlights: knowledge pipeline, etc.).

• n8n

  • Pricing page (cloud plans, run counts, concurrency).
  • License docs (Sustainable Use License announcements/terms).
  • Funding coverage (growth trend).

• Make

  • Pricing page (credit model / change notices).
  • Review overviews (tiers / getting started).
  • Recent reviews summarizing strengths (branching, logs, cost efficiency, connector breadth).

• Supplement

  • Discussion of the shift from RAG to agent-oriented architectures and implications for AI-app design.