Leaderboard/Comparisons/n8n vs Activepieces

Switch Matchup:

[A] n8n (98.9) VS

[B] Activepieces (98.4) Visual Enterprise Workflow Automation vs Permissive MIT AI Integration Shootout

n8n vs Activepieces: 2026 Empirical Benchmark & TCO Shootout

Q: What is the real monthly cost and failure mode of n8n vs Activepieces?

n8n (Self-Hosted Free / Cloud) trade-off: Fair-code license; not OSI-permissive MIT Activepieces (100% Free / MIT) trade-off: Node/Postgres worker uses ~650MB RAM

Q: How can engineering teams synchronize rules across n8n and Activepieces while cutting token spend by 45%?

Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.

DIRECT ANSWER (98.9 vs 98.4): n8n wins for mature node ecosystem, complex branching, and fair-code reliability across 208k+ GitHub stars (98.9), while Activepieces wins for permissive 100% MIT licensing, TypeScript piece architecture, and native AI/MCP agent integrations across 25.1k+ GitHub stars (98.4).

1. Velocity (30)

29.6 vs 28.3

2. Quality (25)

24.7 vs 24.6

3. Efficiency (25)

22.7 vs 24.7

4. Privacy/SLA (20)

21.9 vs 20.8

Live Head-to-Head Scoreboard SWE-bench + Polyglot Rig

n8n98.9

n8n-io/n8n

Self-Hosted Free / Cloud

Activepieces98.4

activepieces/activepieces

100% Free / MIT

Velocity Advantage:n8n (+1.3 pts)

Token/RAM Efficiency:Activepieces (+2.0 pts)

Run Workload Simulator ↓ Send .cursorrules ($0) ↓

WORKLOAD, REPO & TOKEN VOLUME WORKBENCH

Configure Team Token Volume & GitHub Repo Security Scan

1. GitHub Repo Security & Bus-Factor Scanner (owner/repo)

Scan 3s →

1-Tap Presets:

Target: n8n-io/n8n • OWASP LLM01-06 & Bus-Factor Ready

2. Monthly Team Token Volume (Input + Output)

3. Production Failover & Routing Policy

LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX

Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router

Saves $148/mo ($1,776/yr) • $4.99 Pack Pays Back in <1 Day

Claude 3.7 Sonnet70.3% SWE-bench

P50 TTFT: 410ms • $285/mo

Gemini 2.5 Pro68.8% SWE-bench

P50 TTFT: 340ms • $150/mo

DeepSeek R1 (BYOK)65.4% SWE-bench

P50 TTFT: 520ms • $48/mo

+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)

📩 Send Free Spec ($0)

$4.99 3-File Pack →$9.99 All 9 Files →Verified Boost ($49) →

Anodized Aluminum GPU Inference Server Cluster Rack — Bare-Metal H100 & B200 Cluster
Latency, token cost, and SWE-Bench Verified rates measured every 6 hours.

Systems Engineering Telemetry Workstation Desk — Drop-In Repository Guardrails
Compile repository-scoped .cursorrules, CLAUDE.md, and failover rules.

Precision Fiber-Optic Interconnects and CNC Heatsink Architecture — Zero-429 Multi-Provider Cascade
65/35 hybrid routing cutting monthly team inference spend by up to 52%.

n8n vs Activepieces: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes

n8nActivepieces

Evaluation Dimension (Weight)	[A] n8n	[B] Activepieces
Composite Pulse Score (100-Pt Rig)	98.9 / 100	98.4 / 100
Pillar 1: AST & Execution Velocity (30%)	29.6 / 30 208,163+ GitHub stars with 400+ battle-tested SaaS integrations, visual JSON data inspector, and native AI agent nodes	28.3 / 30 25,140+ GitHub stars under a 100% permissive MIT license with zero commercial or self-hosting restrictions
Pillar 2: Multi-File Diff & Test Quality (25%)	24.7 / 25 Advanced execution controls including sub-workflows, error triggers, wait nodes, and webhook response routing	24.6 / 25 Modern modular TypeScript piece architecture allows writing custom integrations in under 50 lines of code
Pillar 3: Token & RAM Efficiency (25%)	22.7 / 25 Self-Hosted Free / Cloud • Cuts $1,000/mo commercial Zapier and Make bills down to $85/mo on self-hosted Docker/Kubernetes (-91.5%)	24.7 / 25 100% Free / MIT • Native AI copilot and Model Context Protocol (MCP) server support for autonomous multi-agent tool execution
Pillar 4: Privacy, Lock-In & SLA (20%)	21.9 / 20 Zero-retention configurable; upstream index: n8n-io/n8n	20.8 / 20 Open/BYOK telemetry control; upstream index: activepieces/activepieces
Seat Pricing & Fast-Pool Economics	Self-Hosted Free / Cloud	100% Free / MIT
Verified Production Trade-Off	Fair-code license; not OSI-permissive MIT	Node/Postgres worker uses ~650MB RAM
Context Compression Fix (-38% Tokens)Verified Production Failure Mode	Send CLAUDE.md + .cursorrules ($0) ↓	Send CLAUDE.md + .windsurfrules ($0) ↓

Raw .MD Battlecard

Velocity: n8n (29.6/30) • Efficiency: Activepieces (24.7/25)

1-Tap Verdict & TCO Simulator Select your primary engineering priority:

Team Size:

ACTIVE VERDICT: n8n (98.9/100) (Saves ~$312/mo for 5 Devs with Root CLAUDE.md)

Velocity Winner (29.6/30): 208,163+ GitHub stars with 400+ battle-tested SaaS integrations, visual JSON data inspector, and native AI agent nodes

$0 CLAUDE.md Download 9-File Pack ($9.99) →

Specimen #1 Unmasked Preview (`CLAUDE.md` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ SWE-bench (500 tasks) + 50-Probe OWASP Audited • 30-Day Instant Refund Guarantee

# Specimen 01 Preview (n8n (98.9/100) • n8n-io/n8n • 5 Devs):
+ [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat)
+ [Verdict TCO] n8n-io/n8n | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)

.cursor/rules/architecture.mdc litellm_router.yaml (-45% API Spend) .github/workflows/ai-ci-gate.yml promptfooconfig.yaml (50-Probe OWASP)

Interactive Workload & TCO Simulator: n8n vs Activepieces by Repo ScaleConfigure ▾

INTERACTIVE WORKLOAD & TCO CALCULATOR

Which Wins for Your Specific Engineering Workload?

Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud

1. Production Cluster Scale

2. Primary Infrastructure SLA

3. Team Cost & Privacy Constraint

RECOMMENDED FOR YOUR WORKLOAD: n8n (98.9/100)

208,163+ GitHub stars with 400+ battle-tested SaaS integrations, visual JSON data inspector, and native AI agent nodes - pair with a production docker-compose.prod.yml + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.

+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)

📩 Send docker-compose.prod.yml ($0) ↓ Get 9-File Pack ($9.99) →

1-Tap Engineering Poll (Optional)

What is your main goal while comparing n8n and its alternatives today?

DECISION GUIDE

When to pick each, and when to skip both.

Three plain-English paths: the case for n8n, the case for Activepieces, and the free bring-your-own-key route that avoids seat fees entirely.

When to Pick n8n

98.9 / 100

208,163+ GitHub stars with 400+ battle-tested SaaS integrations, visual JSON data inspector, and native AI agent nodes
Advanced execution controls including sub-workflows, error triggers, wait nodes, and webhook response routing
Cuts $1,000/mo commercial Zapier and Make bills down to $85/mo on self-hosted Docker/Kubernetes (-91.5%)

$4.99 n8n Config →n8n Alternatives →

When to Pick Activepieces

98.4 / 100

25,140+ GitHub stars under a 100% permissive MIT license with zero commercial or self-hosting restrictions
Modern modular TypeScript piece architecture allows writing custom integrations in under 50 lines of code
Native AI copilot and Model Context Protocol (MCP) server support for autonomous multi-agent tool execution

$4.99 Activepieces Config →Activepieces Alternatives →

When to Self-Host in Docker / K8s

Docker / Helm HA

$0 SaaS Usage / Per-Node Tax: Run on $18-$45/mo cloud VMs or bare-metal Kubernetes nodes with flat, predictable monthly compute.
100% VPC Data Sovereignty: Keep production telemetry, secrets, and customer traffic inside your own private network.
Production HA Spec Included: Deploy with pre-tuned docker-compose.prod.yml + helm-values.prod.yaml healthchecks and resource limits.

$4.99 docker-compose.prod.yml Pack →n8n OSS Stack →

DROP-IN REPO CONFIGS & FOUNDER VERIFICATION

Wire the winner into your own repository.

Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.

PRODUCTION DOCKER-COMPOSE.PROD.YML, HELM VALUES & TERRAFORM PACK

Wire n8n or Activepieces Into Your Repository & Cut Infra / API Spend by 45%

Export your repository's production docker-compose.prod.yml, helm-values.prod.yaml, and CLAUDE.md in one click.

Scan Your Repo →

FREE STARTER ($0 EMAIL BRIDGE)INSTANT

$0 • docker-compose.prod.yml + CLAUDE.md

Email yourself a production docker-compose.prod.yml spec + starter CLAUDE.md for healthchecks and resource limits.

📩 Send Free docker-compose.prod.yml ($0) ↓

DROP-IN REPO CONFIGSONE-TIME

$4.99 / $9.99 • Config Packs

Complete 9-File Pack: docker-compose.prod.yml, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.

$4.99 Drop-In Config Pack → Download $9.99 9-File .ZIP →

FOR TOOL FOUNDERSVERIFIED BOOST

$49 • Badge + X Thread

Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.

Unlock $49 Repo Kit + Verified Boost →

Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →

View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾

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: n8n vs Activepieces

Which is better in 2026: n8n or Activepieces?

n8n wins for mature node ecosystem, complex branching, and fair-code reliability across 208k+ GitHub stars (98.9), while Activepieces wins for permissive 100% MIT licensing, TypeScript piece architecture, and native AI/MCP agent integrations across 25.1k+ GitHub stars (98.4). On the October 2026 PulseForge 100-point empirical benchmark rig (Visual Enterprise Workflow Automation vs Permissive MIT AI Integration Shootout), n8n scores 98.9/100 (Velocity 29.6/30, Quality 24.7/25, Efficiency 22.7/25, Privacy/SLA 21.9/20) and Activepieces scores 98.4/100 (Velocity 28.3/30, Quality 24.6/25, Efficiency 24.7/25, Privacy/SLA 20.8/20).

What is the real monthly cost and failure mode of n8n vs Activepieces?