aitaskmatch
AI Automation

n8n Review 2026

Self-hosting is a major differentiator; execution-based pricing with unlimited workflow steps.

AITaskMatch ResearchBrief dated 7 Sept 2026Not yet rated

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Research brief · 7 Sept 2026. This overview is based on supplied product research, not a hands-on test. Check the provider for current terms and limits. Our methodology

What stands out

  • Self-hosting is a major differentiator
  • Execution-based pricing with unlimited workflow steps
  • Visual + code hybrid
  • Strong AI agent/RAG/multi-agent capabilities
  • Human approvals/guardrails/observability
  • Free Community Edition
  • 30% recurring first-year affiliate

What to consider

  • Self-hosting adds infrastructure responsibility
  • Technical power can increase learning curve
  • Cloud has infrastructure/configuration restrictions
  • Business self-hosted tier is expensive
  • Integration counts vary by how n8n defines nodes/apps; avoid simplistic count wars
  • Affiliate commission only applies to n8n Cloud
  • Paid ads are prohibited for affiliates

Starter

Custom
  • n8n Cloud; 2.5K executions; unlimited steps/users; 1 shared project; 5 concurrent executions; 2,300 AI credits/mo.

Pro

Custom
  • n8n Cloud; 10K executions; 3 shared projects; 20 concurrent executions; up to 13,700 AI credits/mo; admin/global variables/history/search.

Business

Custom
  • Self-hosted; 40K executions; SSO/SAML/LDAP; environments; Git version control; scaling; AI Assistant preview.

Enterprise

Custom
  • Cloud or self-hosted; custom executions; unlimited shared projects; 200+ concurrency; secrets/log streaming; SLA/support.

Community Edition

$0
  • Basic self-hosted edition available from GitHub; infrastructure/ops are the user's responsibility.
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Albato

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Product research

Browse AI

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Best for: E-commerce monitoring

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How this overview was prepared

Based on dated 7 Sept 2026. A hands-on evaluation and editorial rating are still pending. Product information should be checked against the provider’s current terms.

Read our editorial methodology