n8n Self-Hosted vs Cloud: Complete Pricing Guide for Workflow Automation

TL;DR

n8n offers two deployment paths: self-hosted (free and open-source) or cloud-hosted with tiered pricing. Self-hosted n8n runs on your infrastructure with no licensing fees, while n8n Cloud provides managed hosting across Starter, Pro, and Enterprise tiers with varying execution limits and features.

Self-hosted deployments require server management but give you complete control over data, unlimited workflow executions, and no per-execution costs. Install with npm install -g n8n or run via Docker on port 5678. You handle updates, backups, SSL certificates, and scaling. Infrastructure costs depend on your hosting provider and workflow complexity – a basic VPS can run simple workflows, while high-volume automation may need dedicated servers or Kubernetes clusters.

n8n Cloud eliminates infrastructure management. The platform handles updates, security patches, and scaling automatically. Starter tier suits individual users and small teams testing automation. Pro tier adds advanced features like environment variables, custom domains, and priority support for growing teams. Enterprise tier provides dedicated resources, SSO, and SLA guarantees for organizations with compliance requirements.

Both deployment options support the same 400+ integrations and AI capabilities through dedicated AI Agent and AI Chain nodes. Self-hosted users can integrate OpenAI, Anthropic, or local LLM endpoints without additional n8n fees. Cloud users access the same AI nodes but may face execution limits based on their tier.

Choose self-hosted if you need data sovereignty, have DevOps resources, or run high-volume workflows where per-execution pricing becomes expensive. Choose cloud if you want zero maintenance overhead, need quick deployment, or prefer predictable monthly costs over infrastructure management.

Most teams start with n8n Cloud for rapid prototyping, then migrate to self-hosted as workflow volume grows. Test both approaches with a simple workflow connecting a webhook trigger to an AI Agent node processing customer inquiries before committing to either path.

Caution: Always validate AI-generated workflow configurations and API credentials in a test environment before deploying to production systems handling sensitive data.

Understanding n8n’s Pricing Models

n8n offers two distinct pricing approaches that cater to different organizational needs and technical capabilities. The self-hosted version is completely free and open-source, allowing unlimited workflows, executions, and users without any licensing fees. You only pay for the infrastructure where you deploy it – whether that’s a cloud VPS, on-premises server, or container orchestration platform.

The n8n Cloud offering operates on a tiered subscription model with Starter, Pro, and Enterprise plans. Each tier provides managed hosting, automatic updates, and built-in security features without requiring infrastructure management. Cloud plans typically charge based on workflow executions rather than a flat monthly rate, making costs predictable for teams with consistent automation volumes.

When running n8n self-hosted, your primary expenses include server resources, storage for workflow data and execution logs, and optional backup solutions. A typical deployment handling moderate workflow volumes runs comfortably on a 2-core instance with 4GB RAM. Database costs depend on your choice – PostgreSQL or MySQL for production environments, SQLite for development.

AI integration costs apply equally to both deployment models. When you connect AI Agent or AI Chain nodes to services like OpenAI, Anthropic, or local LLM providers, those API calls incur charges from the respective AI vendor. Self-hosted deployments give you complete control over which AI providers you use and how you manage those API keys through environment variables.

Execution-Based vs Infrastructure-Based Costs

Cloud pricing scales with your automation activity, making it straightforward to budget for growing teams. Self-hosted costs remain relatively fixed regardless of execution volume, which benefits organizations running thousands of workflows daily. However, self-hosted requires dedicated DevOps resources for maintenance, security patches, and scaling infrastructure as your automation needs expand.

Consider your team’s technical expertise and workflow volume when evaluating these models. Organizations with existing infrastructure teams often find self-hosted more economical long-term, while smaller teams prefer cloud’s simplicity despite higher per-execution costs.

Self-Hosted Cost Breakdown: Infrastructure and Maintenance

Self-hosting n8n eliminates subscription fees but introduces infrastructure and operational costs that vary based on your deployment scale and requirements.

A basic n8n deployment runs comfortably on a virtual private server with 2 CPU cores and 4GB RAM. Providers like DigitalOcean, Linode, and Hetzner offer suitable instances. Larger teams running complex workflows with AI integrations typically need 4-8GB RAM to handle concurrent executions and AI node processing.

Storage requirements grow with workflow execution history and file attachments. Most teams start with 50-100GB and scale based on retention policies. Database hosting adds another consideration – n8n supports PostgreSQL for production deployments, which you can run on the same server or use a managed database service.

Maintenance and Operations

Self-hosted n8n requires regular updates, security patches, and backup management. Teams typically allocate several hours monthly for maintenance tasks including:

• Updating n8n to the latest version via npm or Docker image pulls
• Monitoring workflow execution logs and debugging failures
• Managing SSL certificates for secure external access
• Configuring environment variables like N8N_EDITOR_BASE_URL for proper webhook routing

AI workflow integrations introduce additional considerations. When using AI Agent or AI Chain nodes with external LLM providers, you pay API costs directly to OpenAI, Anthropic, or other providers. These costs scale with usage volume and model selection.

# Example Docker deployment with environment configuration
docker run -d \
  -p 5678:5678 \
  -e N8N_EDITOR_BASE_URL=https://n8n.yourdomain.com \
  -e DB_TYPE=postgresdb \
  -e DB_POSTGRESDB_HOST=postgres \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n

Caution: Always validate AI-generated deployment commands against official n8n documentation before running in production environments. Deprecated environment variables can cause authentication failures.

n8n Cloud Pricing Tiers Explained

n8n Cloud operates on a tiered subscription model with three main plans: Starter, Pro, and Enterprise. Each tier provides managed hosting, automatic updates, and built-in security without requiring infrastructure management.

The Starter tier targets individuals and small teams testing workflow automation. You get access to the full visual editor, all 400+ integrations, and basic AI nodes like AI Agent and AI Chain. Execution limits apply, making this suitable for low-volume workflows such as daily lead enrichment or weekly report generation. The tier includes community support through forums and documentation.

Pro Tier

Pro unlocks higher execution limits and adds team collaboration features. Multiple users can edit workflows simultaneously, with role-based access controls for production environments. This tier suits growing teams running customer onboarding sequences, multi-step data pipelines, or AI-powered content workflows that process hundreds of records daily. You gain priority email support and access to advanced debugging tools.

Enterprise Tier

Enterprise provides custom execution limits, dedicated support channels, and SLA guarantees. Organizations running mission-critical automation – like real-time inventory sync across e-commerce platforms or AI-driven customer service routing – benefit from this tier. You can negotiate custom contracts based on workflow complexity and execution volume.

AI Integration Considerations

All cloud tiers support AI nodes for OpenAI, Anthropic, and other providers. However, API costs for these services bill separately through your provider accounts. When building AI Agent workflows that call external language models, factor in both n8n subscription costs and per-token charges from your AI provider.

Caution: Cloud tiers limit concurrent executions and workflow complexity. Test AI-heavy workflows in development environments before deploying to production. Monitor execution times when chaining multiple AI nodes, as timeouts can occur with complex agent reasoning loops.

Total Cost of Ownership Comparison

Self-hosted n8n requires server resources that scale with workflow complexity. A basic DigitalOcean droplet or AWS EC2 instance handles light automation workloads, while production deployments with AI integrations demand more compute power. Teams running AI Agent nodes for document processing or customer support typically provision instances with dedicated CPU and memory to avoid timeout issues.

Cloud hosting eliminates infrastructure management but introduces per-execution pricing. The Starter tier works for small teams with predictable workflow volumes, while Pro and Enterprise tiers accommodate higher execution limits and advanced features like SSO.

Hidden Operational Expenses

Self-hosted deployments accumulate maintenance costs beyond server bills. Database backups, SSL certificate renewal, security patches, and monitoring tools require ongoing attention. Teams often underestimate the time spent troubleshooting Docker networking issues or debugging webhook failures after system updates.

# Example backup script for self-hosted PostgreSQL
docker exec n8n-postgres pg_dump -U n8n > backup-$(date +%Y%m%d).sql

AI integration costs apply to both deployment models. OpenAI API calls, Anthropic Claude requests, and vector database operations bill separately regardless of hosting choice. A workflow processing customer emails through an AI Agent node incurs identical API costs whether running on n8n Cloud or a self-hosted instance.

Break-Even Analysis

Small teams with fewer than five active workflows often find cloud hosting more economical when factoring in setup time and maintenance overhead. Organizations running dozens of workflows with high execution volumes typically achieve cost savings through self-hosting after the initial infrastructure investment.

Consider AI workload patterns carefully. Workflows with sporadic AI Agent usage benefit from cloud’s pay-per-execution model, while continuous AI processing favors self-hosted deployments where you control compute allocation. Always validate AI-generated workflow configurations in a test environment before production deployment to avoid unexpected API costs.

Performance and Scalability Considerations

Self-hosted n8n instances scale based on workflow complexity and execution frequency. A basic deployment runs comfortably on 2GB RAM and 2 CPU cores for teams processing hundreds of workflows daily. Heavy AI workloads using AI Agent or AI Chain nodes require additional resources since these nodes maintain conversation context and process larger payloads.

Database choice significantly impacts performance. PostgreSQL handles concurrent workflow executions better than SQLite for production environments. Configure connection pooling to prevent bottlenecks when multiple workflows trigger simultaneously:

docker run -d \
  -p 5678:5678 \
  -e DB_TYPE=postgresdb \
  -e DB_POSTGRESDB_HOST=postgres \
  -e DB_POSTGRESDB_PORT=5432 \
  -e DB_POSTGRESDB_DATABASE=n8n \
  -e DB_POSTGRESDB_USER=n8n \
  -e DB_POSTGRESDB_PASSWORD=n8n \
  -e EXECUTIONS_MODE=queue \
  n8nio/n8n

Queue mode separates workflow execution from the main process, preventing UI freezes during intensive operations. This becomes essential when integrating OpenAI, Anthropic, or local LLM endpoints that introduce variable latency.

Cloud Infrastructure Advantages

n8n Cloud handles scaling automatically without manual intervention. The platform manages database optimization, queue workers, and infrastructure updates. Teams avoid capacity planning exercises and can focus on workflow design rather than server maintenance.

Cloud deployments benefit from geographic distribution and redundancy built into the platform. Self-hosted teams must implement their own backup strategies, monitoring systems, and failover configurations.

Caution: When using AI nodes with external APIs, implement rate limiting and error handling regardless of deployment type. AI providers enforce their own quotas, and poorly designed workflows can exhaust API limits quickly. Test AI-integrated workflows with small datasets before scaling to production volumes.

Execution Limits and Throttling

Self-hosted instances have no artificial execution limits beyond hardware capacity. Cloud tiers impose workflow execution quotas that vary by plan level, making self-hosting attractive for high-volume automation scenarios.

Step-by-Step Setup: Self-Hosted n8n on Docker

Before deploying n8n with Docker, ensure you have Docker and Docker Compose installed on your server. You’ll also need a domain name if you plan to expose n8n externally with SSL.

Create a dedicated directory for your n8n installation and a docker-compose.yml file:

mkdir n8n-docker && cd n8n-docker
touch docker-compose.yml

Basic Docker Compose Configuration

Here’s a production-ready configuration that includes persistent storage and environment variables:

version: '3.8'

services:
  n8n:
    image: n8nio/n8n
    restart: always
    ports:
      - "5678:5678"
    environment:
      - N8N_EDITOR_BASE_URL=https://n8n.yourdomain.com
      - WEBHOOK_URL=https://n8n.yourdomain.com/
      - GENERIC_TIMEZONE=America/New_York
    volumes:
      - ./n8n_data:/home/node/.n8n

Replace n8n.yourdomain.com with your actual domain. The N8N_EDITOR_BASE_URL variable ensures webhooks and external integrations work correctly.

Launch and Access

Start the container with:

docker-compose up -d

Access n8n at http://localhost:5678. On first launch, you’ll create an owner account. This replaces the deprecated basic auth system that older tutorials reference.

Adding AI Integration Capabilities

For workflows using AI nodes like AI Agent or AI Chain, add API keys as environment variables:

environment:
  - OPENAI_API_KEY=your_key_here
  - N8N_EDITOR_BASE_URL=https://n8n.yourdomain.com

Caution: Never commit API keys to version control. Use Docker secrets or external secret management for production deployments. Always validate AI-generated workflow configurations in a test environment before deploying to production systems.

Backup Strategy

Your workflow data persists in the ./n8n_data volume. Schedule regular backups of this directory to prevent data loss during system failures or migrations.