Can I migrate from Make to n8n if I outgrow Make's pricing?

Yes, though it requires rebuilding workflows rather than a direct import. Most Make scenarios have direct n8n equivalents. The migration effort is typically 2-4 hours per complex workflow. We recommend migrating your highest-volume workflows first to capture the most cost savings.

What are the minimum server specs for self-hosting n8n in production?

For up to 100,000 executions per month: 2 vCPU, 4GB RAM, 40GB SSD. This comfortably runs n8n with PostgreSQL and Redis on a single VPS. For higher volumes or concurrent workflow processing, use queue mode with separate workers. A €12/month Hetzner CX22 handles this spec.

Does n8n have a visual editor or is it code-only?

n8n has a visual drag-and-drop workflow editor very similar to Make's. Most nodes are configured visually with form-based inputs. The Code node is optional, you only need it when you want to run custom JavaScript or Python logic. Non-technical users can build functional workflows entirely in n8n's visual editor.

How do I connect Make or n8n to Supabase?

Both have Supabase integrations. Make has a native Supabase module. n8n has a Supabase node for standard table operations plus an HTTP Request node for Supabase's full REST API. For both platforms, you authenticate with your Supabase project URL and API key. We recommend using the service role key for automation workflows (they bypass RLS, run on a server, not in a user's browser).

Is Make or n8n better for building AI agent workflows?

n8n is significantly better for AI workflows. Its native LangChain integration, AI Agent nodes, vector store connections (Pinecone, Supabase pgvector), and memory nodes make complex AI automation first-class. Make supports AI via HTTP requests to OpenAI and Anthropic APIs but lacks the native agent orchestration primitives that n8n provides.

n8n vs Make (2026): Which Automation Tool Wins?

Make and n8n are the two most powerful automation platforms available in 2026. This guide is about how that choice actually plays out once you are building real workflows: pricing at volume, self-hosting tradeoffs, error handling, and where each tool wins in production. For the full side-by-side feature and pricing breakdown, see the n8n vs Make comparison. After building hundreds of automation workflows on both platforms, here is how the decision actually plays out in practice.

The Core Philosophy Difference

Make is a cloud-hosted automation platform: clean UI, 1,000+ native integrations, and a pricing model based on monthly operations. It's designed for business teams and non-technical builders who want powerful automation without infrastructure management.

n8n is an open-source automation platform that can be self-hosted. It has 400+ integrations, supports custom JavaScript and Python code nodes, and has no per-operation pricing. It's designed for technical teams who need data ownership, complex logic, or very high automation volumes.

Pricing: n8n Wins at Scale

Make pricing: from $9/month for 10,000 operations/month. At serious automation volumes (1M+ operations/month), you're paying $299-$799/month just for Make.

n8n pricing: $20/month for the cloud version (unlimited executions on Fair Usage). Or self-host on a $5/month VPS and pay nothing.

For teams running high-volume automations, lead processing, data sync, bulk notifications, n8n's economics are dramatically better. For teams running fewer, simpler automations, Make's UX advantage often justifies the cost.

Make Wins on UX and Integrations

Make's visual scenario builder is genuinely beautiful. The canvas layout, the module inspector, and the built-in error handling UI are all polished. For non-technical team members building and maintaining automations, Make is significantly more accessible.

Make also has more native integrations (1,000+ vs n8n's 400+). If your automation requires a niche tool (specific CRM, specific email platform), Make is more likely to have a native connector without custom HTTP requests.

n8n Wins on Technical Power

n8n's Code node lets you write arbitrary JavaScript or Python inside your automation flow. This means n8n can do anything Make cannot: complex data transformations, calling internal databases directly, running ML model inference.

n8n's LangChain nodes (released in 2024) make it the leading platform for building AI agent workflows: you can chain LLM calls, use vector stores, and orchestrate multi-agent flows natively.

For technical teams, n8n is simply more powerful.

Data Privacy and Self-Hosting

If your automation handles sensitive data (healthcare, financial, legal), self-hosting n8n is the correct choice. Your automation data never leaves your infrastructure.

Make is cloud-only. For most European clients with GDPR obligations involving sensitive personal data, we recommend n8n self-hosted on a EU-region VPS.

Self-Hosting n8n: What It Actually Takes

Self-hosting n8n is more accessible than most developers expect. The minimum viable setup is a $6/month Hetzner or DigitalOcean VPS (2GB RAM, 2 vCPU), Docker, and a reverse proxy (Caddy or Nginx with Let's Encrypt for HTTPS). The entire setup takes about 2 hours following n8n's official Docker Compose documentation.

For production-grade self-hosted n8n, you need three additional components: a persistent PostgreSQL database (for workflow data and execution history), a Redis instance (for queue mode, essential if you have multiple workflows or high concurrency), and a backup strategy for both databases. Running n8n in queue mode on a single VPS with PostgreSQL and Redis handles thousands of executions per hour reliably.

The ongoing maintenance cost is real but manageable. You need to update n8n when new versions release (typically monthly), monitor disk usage for execution logs, and apply VPS OS security patches. Budget 1-2 hours per month for maintenance. For the economics to work in favour of self-hosting, you need to be running more than ~200,000 Make operations per month, at that point, the €5-€20/month VPS cost beats Make's $80-$200/month plan.

Trigger Types Comparison

Both platforms support webhooks, scheduled triggers, and polling, but with important differences in implementation. Make's webhook triggers are instant (real-time push) and support custom headers and authentication. n8n's webhook triggers are equally capable and, when self-hosted, avoid any data leaving your infrastructure.

For scheduled workflows, Make uses a cron-like scheduler with a minimum interval of 1 minute on paid plans and 15 minutes on free. n8n supports full cron syntax (including sub-minute schedules on self-hosted) and can be triggered on exact cron expressions like 0 9 * * 1 (every Monday at 9am). For workflows that need precise timing, n8n's scheduler is more flexible.

Both platforms support app-native event triggers (e.g., 'when a new row is added in Airtable', 'when a Stripe payment succeeds'). Make tends to have more native event triggers out of the box thanks to its larger integration library. For Make, these are typically webhook-based with automatic setup. For n8n, some triggers require polling rather than webhooks, which means a delay between the event and the workflow execution.

Error Handling

Make has one of the best error handling UIs of any automation platform. Each scenario can have error handlers defined graphically, route the workflow to a different path when a specific module fails, or configure global error handlers that send a Slack alert and log to a Google Sheet. Non-technical users can configure meaningful error handling without understanding code.

n8n's error handling is more powerful but requires more configuration. Error workflow nodes can catch failures in any workflow and trigger a separate error-handling workflow. The Error Trigger node receives the full error context: which node failed, the input data, the error message, and the workflow execution ID. This makes programmatic error handling (logging to a database, retrying with backoff, alerting with full context) more capable than Make's visual handlers.

For production workflows that process important business data, order confirmations, user notifications, financial events, error handling is not optional. Our standard n8n setup for production workflows: an error handler workflow that logs failures to a Supabase table with timestamp, workflow name, input data, and error message, plus a Slack alert with a link to the failed execution. This gives full observability without needing to check n8n's execution log manually.

Cost at Volume: 10K+ Runs Per Month

At 10,000 workflow runs per month, Make's pricing depends heavily on the number of operations per run. A simple 3-step workflow (trigger + transform + send) consumes 3 operations per run = 30,000 operations/month = Make's Core plan at $9/month. Comfortable.

At 100,000 runs per month with 5 steps each = 500,000 operations/month. Make's Pro plan ($16/month) covers 150,000 operations. You need Teams at $29/month for 800,000 operations. Starting to add up.

At 500,000 runs/month with 10 steps each = 5 million operations/month. Make charges $119-$299/month at this volume. n8n self-hosted on a €20/month VPS (4GB RAM) handles this volume comfortably. The annual savings: €1,200-€3,000. Over 3 years, that is a material infrastructure cost difference.

The break-even point where n8n self-hosting becomes cheaper than Make is roughly 300,000-400,000 operations per month, accounting for VPS and maintenance costs. Below that threshold, Make's UX and zero-maintenance advantages often win on total cost.

Community and Templates

Make's template library has 500+ pre-built scenarios covering common integration patterns: CRM sync, form-to-email, e-commerce order processing, social media scheduling. For non-technical teams getting started, these templates dramatically reduce time-to-first-working-automation.

n8n's community is smaller but highly technical. The n8n community forum (community.n8n.io) is active with detailed solutions to complex workflow problems. The official n8n workflow template library has 700+ workflows, and the community has contributed thousands more. For technical teams, the depth of community solutions for complex patterns (AI agent workflows, database sync, API integrations) is often better than Make's.

For AI workflow templates specifically, n8n has a clear lead. Its LangChain integration, AI agent nodes, and vector store connections are first-class features with extensive template coverage. Teams building AI-powered automations, document processing, intelligent routing, LLM-backed data enrichment, will find n8n's community resources significantly richer for these use cases than Make's.

Our Verdict

Use Make when: your team is non-technical, you want the best UX, you need 1,000+ native integrations, and your volume is under 500K operations/month.

Use n8n when: you have GDPR/data residency requirements, your volume is high (>500K ops/month), you need custom code logic, or you're building AI agent workflows.

At App Studio, we use Make for most client automation projects (better UX for client team handovers) and n8n for our own internal automations and AI-heavy workflows. Compare the full feature set in our n8n vs Make comparison.

n8n vs Make in 2026: Which Automation Tool Should You Choose?