Dify vs n8n: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (94.7 vs 95.1): n8n wins for end-to-end business workflow orchestration across 400+ SaaS integrations and webhooks with 207.6k+ GitHub stars and a 480MB RAM footprint (95.1), while Dify wins for LLM-first visual RAG pipelines, prompt IDE management, and 1-click chat API publishing with 158.6k+ GitHub stars (94.7). On the October 2026 PulseForge 100-point empirical benchmark rig (Visual AI Workflow Builder & Agent Automation Shootout), Dify scores 94.7/100 (Velocity 28.6/30, Quality 23.8/25, Efficiency 23.1/25, Privacy/SLA 19.2/20) and n8n scores 95.1/100 (Velocity 28.1/30, Quality 23.8/25, Efficiency 24.3/25, Privacy/SLA 18.9/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
Which Wins for Your Specific Engineering Workload?
158,638+ GitHub stars with built-in hybrid RAG chunking, reranking, and multi-model prompt IDE โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Dify vs n8n: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Dify | n8n |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.7 / 100 | 95.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.6 / 30 โ 158,638+ GitHub stars with built-in hybrid RAG chunking, reranking, and multi-model prompt IDE | 28.1 / 30 โ 207,626+ GitHub stars with 400+ battle-tested SaaS, database, and webhook triggers |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.8 / 25 โ 1-click publishing of visual agent workflows as OpenAI-compatible REST APIs and embeddable web apps | 23.8 / 25 โ Lightweight 480MB Node/TypeScript container (-67% RAM vs Dify multi-container Docker stack) |
| Pillar 3: Token & RAM Efficiency (25%) | 23.1 / 25 โ Native MCP tool client/server orchestration and dataset evaluation logs | 24.3 / 25 โ Execution-based self-hosting eliminates per-step Zapier/Make automation bills |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.2 / 20 โ Repo index: langgenius/dify | 18.9 / 20 โ Repo index: n8n-io/n8n |
| Seat Pricing & Fast-Pool Economics | Open Source / Cloud | Self-Hosted Free / Cloud |
| Verified Production Failure Mode | Multi-container Docker uses ~1.4GB RAM | Fair-code license; not OSI-permissive MIT |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Dify
94.7 / 100- 158,638+ GitHub stars with built-in hybrid RAG chunking, reranking, and multi-model prompt IDE
- 1-click publishing of visual agent workflows as OpenAI-compatible REST APIs and embeddable web apps
- Native MCP tool client/server orchestration and dataset evaluation logs
When to Standardize on n8n
95.1 / 100- 207,626+ GitHub stars with 400+ battle-tested SaaS, database, and webhook triggers
- Lightweight 480MB Node/TypeScript container (-67% RAM vs Dify multi-container Docker stack)
- Execution-based self-hosting eliminates per-step Zapier/Make automation bills
Make langgenius/dify Run 45% Cheaper Across Both Dify & n8n
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Dify vs n8n
Which is better in 2026: Dify or n8n?
n8n wins for end-to-end business workflow orchestration across 400+ SaaS integrations and webhooks with 207.6k+ GitHub stars and a 480MB RAM footprint (95.1), while Dify wins for LLM-first visual RAG pipelines, prompt IDE management, and 1-click chat API publishing with 158.6k+ GitHub stars (94.7). On the October 2026 PulseForge 100-point empirical benchmark rig (Visual AI Workflow Builder & Agent Automation Shootout), Dify scores 94.7/100 (Velocity 28.6/30, Quality 23.8/25, Efficiency 23.1/25, Privacy/SLA 19.2/20) and n8n scores 95.1/100 (Velocity 28.1/30, Quality 23.8/25, Efficiency 24.3/25, Privacy/SLA 18.9/20).
What is the real monthly cost and failure mode of Dify vs n8n?
Dify (Open Source / Cloud) trade-off: Multi-container Docker uses ~1.4GB RAM n8n (Self-Hosted Free / Cloud) trade-off: Fair-code license; not OSI-permissive MIT
How can engineering teams synchronize rules across Dify and n8n while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.