Make vs n8n: Which Automation Platform is Right for You?

This is one of the most common questions Qynzoo gets, and the honest answer is: it depends on your business. Make is visual, beginner-friendly, and gets you to a working automation in hours. n8n is open-source, self-hostable, and gives you complete control over your data and logic. For a business processing 10,000 or more operations per month, the pricing difference alone can be thousands of euros per year. This guide gives you the framework to decide — not a vague "it depends," but a concrete set of questions with clear answers pointing in one direction.

How Both Platforms Work

Both Make and n8n are built around the concept of a visual workflow canvas — a graphical interface where you connect a trigger (something that starts the workflow) to a series of actions (things that happen as a result). The visual metaphor is the same: you see your automation as a diagram, with nodes or modules connected by lines, and data flowing through them from left to right. This makes both platforms genuinely accessible to non-developers in a way that writing code simply is not. You can see what your automation is doing, inspect the data at each stage, and modify it by clicking rather than typing.

Make calls its building blocks "modules" and organises them into "scenarios." n8n calls them "nodes" and groups them into "workflows." Beyond the naming, there are real architectural differences. Make's modules are heavily guided — each one has a structured configuration panel with dropdown menus and field mappers, keeping complexity low at the cost of flexibility. n8n's nodes are more open, often accepting raw JSON configuration and supporting full JavaScript execution inside the node itself. The implication is significant: Make is faster to get started, n8n is more powerful at the ceiling. Both support webhooks as triggers, scheduled runs on a cron-style timer, and manual execution for testing.

Head-to-Head Comparison

Rather than giving you subjective impressions, here is a factual side-by-side across the dimensions that matter most when choosing a platform. Use this as your reference point before diving into the deeper analysis below.

Feature	Make	n8n
Pricing model	Operations-based. Free: 1,000 ops/mo. Core: €9/mo = 10,000 ops. Pro: €16/mo = 10,000 ops + advanced features.	Execution-based. Self-hosted: free (unlimited). Cloud: $20/mo = 2,500 executions. Pro: $50/mo.
App integrations	1,800+ native app modules	400+ native + HTTP module for any REST API
Self-hostable	No — cloud only	Yes — Docker, VPS, bare metal
Interface	Visual canvas, highly guided	Visual canvas, more open/flexible
Learning curve	Low — productive in 1–2 days	Medium — productive in 3–5 days
Custom code	Limited — JS snippets only in specific modules	Full JavaScript and Python code nodes, unrestricted
AI/LLM support	AI modules for Claude, OpenAI, etc. available	Excellent — deep AI agent nodes, LangChain integration
Best for	SME operators, non-technical teams, fast deployment	Technical teams, data-sensitive businesses, AI agents

Make in Depth — When to Choose It

Make is the right platform when speed and accessibility matter more than raw control. Its visual scenario builder is genuinely excellent — you see data flowing between modules in real time during test runs, which makes debugging intuitive even for non-technical users. The 1,800+ native integrations cover virtually every mainstream SaaS tool: from Google Workspace and Slack to niche platforms like Airtable, Pipedrive, Xero, and ActiveCampaign. If your workflow involves standard business apps and doesn't require unusual data logic, there's a good chance Make has a pre-built module for every step, meaning no HTTP configuration, no API authentication research, and no JSON wrangling.

Where Make truly excels is complex visual logic with conditional branching. The "router" module splits a workflow into multiple paths based on conditions — think of it as an if/else statement you can draw. Combined with Make's aggregators, iterators, and data store modules, you can build genuinely sophisticated workflows without writing a line of code. A real example: an e-commerce order processing workflow that triggers on a new Shopify order, branches based on order value (over €500 goes to a VIP fulfillment route, under goes to standard), updates inventory in a Google Sheet, creates a packing task in Trello, sends a personalised confirmation email via Mailchimp with conditional content blocks based on whether the customer is new or returning, and logs the order to a Notion database. That entire workflow can be built in Make by a non-developer in an afternoon.

The honest concern with Make is pricing at scale. The operations-based model means every module execution counts. A workflow with 10 modules that runs 5,000 times per month consumes 50,000 operations — requiring Make's Teams plan at €29/month for 40,000 ops, meaning you'd need a higher tier. For businesses with high-volume automations, those costs compound across multiple scenarios. If you're processing hundreds of thousands of operations monthly, you need to run a cost projection before committing to Make's pricing model.

n8n in Depth — When to Choose It

n8n is the right platform when you have technical capability and either data privacy requirements or high execution volumes. Its defining advantage is self-hosting: you deploy n8n on a VPS (€5–10/month on Hetzner or DigitalOcean), run it inside your own infrastructure, and your workflow data never leaves your servers. For businesses handling medical records, financial data, legal documents, or any data subject to GDPR audit, this is not a preference — it's a compliance requirement that Make simply cannot meet. n8n's open-source codebase also means you can inspect exactly what the software is doing, which matters in regulated industries.

Where n8n pulls ahead technically is in its code nodes and AI capabilities. Every workflow can include a JavaScript node that executes arbitrary code — connecting to custom APIs, applying proprietary business logic, transforming complex data structures, or running algorithms that no dropdown menu could configure. For AI agent workflows, n8n's native AI agent node (built on LangChain) supports multi-step reasoning, tool use, memory, and looping — capabilities that allow you to build agents that plan and re-plan based on results. A real internal data pipeline example: n8n self-hosted pulls data from five data sources via API every night, runs custom normalization logic in a code node, cross-references customer records between systems, flags discrepancies, generates a summary report using Claude's API, and emails it to the operations team. The entire pipeline runs on a €8/month VPS with zero per-execution costs.

The cost savings example is concrete and worth quantifying. A business running 10,000 automation executions per month pays approximately €29/month on Make's Teams plan. n8n self-hosted on a basic VPS costs roughly €5–8/month with unlimited executions — no per-operation pricing. At 50,000 executions per month the gap widens dramatically: Make would require an enterprise plan at €100+/month, while n8n stays at €8/month. Over a year, that's €1,100+ saved — more than enough to cover a developer's time to manage the server.

The Decision Framework — 5 Questions

Rather than a general recommendation, work through these five questions in order. Each one narrows the decision until you have a clear answer for your specific situation.

What About Zapier?

Zapier is the platform that most people encounter first, and it has a legitimate role for a specific use case: very simple point-to-point automations between mainstream apps, three steps or fewer, at low volume. New Typeform submission goes to Google Sheets — Zapier handles that cleanly, with minimal setup. The moment you need branching logic, loops, custom data manipulation, multiple conditional paths, or any kind of AI integration, both Make and n8n significantly outperform Zapier. Zapier's pricing also escalates quickly; at 5,000 tasks per month you're paying more than Make's Teams plan while getting less capability. For anything beyond the simplest automations, Make or n8n will serve you better — and the migration from Zapier to either is worth the investment.

Qynzoo's Recommendation

We use both platforms in production, and our split is intentional rather than arbitrary. Make handles client-facing SME projects where the client or their operations manager needs to maintain the automation themselves after we build it. Make's lower learning curve means clients can open the scenario, understand what it's doing, and modify it without calling us. That autonomy is valuable. n8n handles our internal infrastructure and every AI agent pipeline we run — because the custom code nodes and AI agent capabilities make complex workflows possible that Make cannot match, and the self-hosted data sovereignty matters for our clients' sensitive data.

For most readers arriving at this comparison: start with Make. The free tier gives you 1,000 operations per month, the Core plan at €9/month gives you 10,000, and the visual interface will get you from zero to a working automation faster than any alternative. Migrate to n8n when you hit one of three triggers: your monthly cost on Make exceeds €40 and you have or can hire someone who is technically comfortable; you identify a data privacy requirement that mandates self-hosting; or you want to build AI agent workflows that require capabilities Make doesn't currently support.

Related Articles