Make (formerly Integromat) is the comparison people skip. Everyone writes “n8n vs Zapier” — and I wrote that post too — but Make is the more interesting competitor. It has a real visual editor, branching logic, error routes, and an operations model that rewards efficient design. If Zapier is the “connect two apps” tool, Make is the “build a flowchart” tool.
So where does n8n actually pull ahead, and where does Make win?
I consult exclusively on n8n, so my bias is disclosed upfront. But I have evaluated Make for clients considering both platforms, and I have migrated teams from Make to n8n when they hit ceilings. This is the honest breakdown.
Quick Verdict
Choose Make if your team values a polished visual editor, you do not need self-hosting, and your workflows stay under a few hundred operations per day. Make’s scenario builder is genuinely the best-looking automation editor in the market.
Choose n8n if you need self-hosting, your workflows require custom code beyond short snippets, you are processing thousands of events per day, or production-readiness patterns like idempotency, dead-letter queues, and structured audit trails are requirements.
Make is a more capable platform than most people give it credit for. The decision comes down to operational requirements, not feature checkboxes.
What Is Make?
Make (rebranded from Integromat in 2022) is a cloud-hosted visual automation platform. Its signature feature is the circular-node scenario editor — a flowchart-style canvas where you connect modules, add routers for branching, and use iterators and aggregators for batch processing. Make has 1,700+ app integrations, a template marketplace, and a pricing model based on operations (each module execution counts as one operation).
Make sits between Zapier and n8n on the complexity spectrum. It handles branching logic and data transformations that would be awkward in Zapier, but it runs into ceilings when workflows need deep custom code, self-hosted infrastructure, or production-grade resilience patterns.
What Is n8n?
n8n is an open-source workflow automation tool — self-hostable or cloud-managed. Workflows are directed acyclic graphs built in a node-based editor. JavaScript and Python code can run inline at any point. 400+ built-in integrations, but any HTTP-accessible API works natively. Error Trigger workflows, sub-workflows with parameters, and environment variable management give you the building blocks for production-grade automation.
The Comparison: 8 Dimensions
1. Visual Editor and UX
Make’s scenario editor is beautiful. The circular module design, the color-coded connections, the way routers fan out into branches — it is the most visually intuitive automation builder on the market. Non-technical users can read a Make scenario and understand the flow without explanation. The drag-and-drop experience is polished, and the module configuration panels are clean and consistent.
n8n’s canvas editor is functional but less refined. Nodes are rectangular, connections are straight lines, and the overall aesthetic is more “developer tool” than “design tool.” The recent n8n UI updates have improved this significantly, but Make still has the edge on pure visual polish. Where n8n’s editor wins is expressiveness — you can see inline code, sub-workflow calls, and error paths directly on the canvas without opening configuration panels.
Winner: Make. The visual editor is Make’s strongest feature. For teams where workflow readability matters (handoffs between team members, documentation, client presentations), Make’s visual language is easier to parse.
2. Pricing at Scale
Make charges per operation — each module execution in a scenario counts as one operation. A 5-module scenario processing 1,000 webhooks per month uses 5,000 operations. Make’s Core plan (10,000 operations/month) runs $10.59/month. The Teams plan (10,000 operations/month with team features) is $18.82/month. Operations scale in purchased bundles — 40,000 operations at $18.82/month, and so on.
This is significantly cheaper than Zapier at equivalent volumes. But it is still per-execution pricing. A high-volume e-commerce operation processing 2,000 orders/day through a 7-module scenario hits 420,000 operations/month — roughly $200-300/month on Make depending on the plan.
n8n self-hosted costs you a VPS ($20-40/month) regardless of execution count. No per-operation pricing. n8n Cloud uses execution-based pricing too, but counts workflow runs rather than individual node operations — a meaningful distinction at scale.
Winner: n8n. Make is cheaper than Zapier, but self-hosted n8n removes per-execution costs entirely. The gap widens at high volumes. At low volumes (under 5,000 operations/month), Make’s free tier is actually competitive.
3. Self-Hosting and Data Sovereignty
Make is cloud-only. All scenario data, execution logs, and API credentials are stored on Make’s infrastructure. Make’s data centers are in the EU and US, and the platform is GDPR-compliant, but you do not control where data is processed or how long execution logs are retained. For regulated industries (HIPAA, PCI DSS, SOC 2 Type II), the inability to run Make inside your own VPC can be a procurement blocker.
n8n runs in a Docker container on any infrastructure you control — your VPC, your Kubernetes cluster, your on-premise server. Webhook payloads never leave your network. Execution logs are stored in your database. Credentials are encrypted with your keys. I have worked with healthcare clients where n8n’s self-hosted model was the only reason the project got compliance approval.
Winner: n8n. If data sovereignty is a requirement, the decision is already made. Make has no self-hosted option and no announced plans for one.
4. Error Handling
Make has surprisingly good error handling for a cloud platform. Error routes let you define what happens when a specific module fails — retry, ignore, commit (continue with partial data), or break (stop the scenario). The error handler module can route failures to different paths. You get scenario-level retry settings and webhook queue management.
But Make’s error handling is still scoped to the scenario level. You cannot fire a completely separate error-handling workflow per failed scenario. You cannot build cross-scenario dead-letter queues. Error metadata is limited to what Make’s runtime captures — you cannot attach custom context to failures.
n8n’s Error Trigger node fires a dedicated workflow when any workflow fails. That error workflow has full access to the failure context — which workflow failed, which node, what the input data was, the error message. You can build centralized error dashboards, route failures by severity to different channels (PagerDuty for critical, Slack for warnings, weekly digest for informational), and implement exponential backoff with custom retry intervals per error type.
Winner: n8n. Make’s error routes are better than Zapier’s binary succeed/fail. But n8n’s ability to run fully independent error-handling workflows with custom logic and cross-workflow error aggregation gives it the edge for production systems.
5. Complexity Ceiling
Make handles complexity better than most cloud platforms. Routers enable multi-branch logic. Iterators and aggregators handle batch processing. You can nest scenarios within scenarios (though it is awkward). Data transformations use Make’s formula syntax — powerful but proprietary.
The ceiling shows up in three places. First, Make scenarios have a maximum of 150 modules per scenario (raised from the original limit but still finite). Second, scheduled scenarios have minimum intervals of 1 minute on paid plans, 15 minutes on free. Third, complex data transformations require learning Make’s formula language — a proprietary syntax that does not transfer to other tools.
n8n workflows have no practical node limit. Scheduling goes down to sub-minute intervals via cron expressions. Data transformations use standard JavaScript — the most widely known programming language. Sub-workflows accept parameters and return data like function calls. Merge nodes combine data from parallel branches cleanly.
Winner: n8n. Make’s ceiling is higher than Zapier’s but still exists. n8n’s graph-based execution model and standard JavaScript avoid the three walls that Make scenarios hit at scale.
6. Custom Code and Extensibility
Make offers a Code module that runs JavaScript, but with strict execution time limits (40 seconds on most plans) and limited library access. You cannot import npm packages, access the filesystem, or make network requests from within the Code module. Make also has a Custom App SDK for building integrations, but it is designed for building connectors, not arbitrary logic.
n8n’s Function nodes run JavaScript or Python with full access to the Node.js runtime. Execution time limits exist on n8n Cloud but are generous, and self-hosted n8n has no time limits at all. You can reference npm packages, call external APIs, manipulate files, and build complex data transformations. n8n also supports community nodes — custom integrations published to npm and installable with one click. If a connector does not exist, you can build one.
Winner: n8n. If your workflows stay within Make’s built-in modules, this dimension does not matter. The moment you need custom logic beyond short snippets, n8n’s open runtime is significantly more capable.
7. Community and Ecosystem
Make has a large and active community. The Make Community forum has tutorials, template sharing, and quick response times. Make’s template marketplace has thousands of pre-built scenarios. Make Academy offers structured learning paths. The platform has a strong following among marketing teams, agencies, and no-code builders.
n8n’s community (community.n8n.io) is more developer-oriented. Workflow sharing, custom node development, and self-hosting guides dominate the conversation. n8n’s template library is growing but smaller than Make’s. n8n’s open-source model means the community also contributes code — bug fixes, new nodes, and integrations flow back into the core product.
Different communities serving different audiences. If you are a marketing operations team, Make’s community has more relevant content. If you are a developer building production infrastructure, n8n’s community speaks your language.
Winner: Tie. Both have active, helpful communities. The fit depends on your team’s technical profile.
8. Production-Readiness
This is the dimension where the gap is widest, and it is the one I care about most.
Production-readiness means: Can this workflow handle duplicate webhook deliveries without creating duplicate records? Can you trace the full execution path of a failed run three weeks later? Can credentials rotate without editing every scenario that uses them? Can failures queue for manual review instead of being silently retried and forgotten?
In n8n, I build all of these patterns routinely. Idempotency via lookup-before-insert with Function nodes. Dead-letter queues via Error Trigger workflows that write failed payloads to a database table for manual review. Structured audit trails via dedicated logging nodes that write to Postgres. Credential rotation via environment variables that propagate to all workflows simultaneously. I wrote an entire 6-dimension checklist covering these patterns.
In Make, several of these patterns are difficult or impossible. Idempotency requires workarounds (check a data store before proceeding, but race conditions exist). Dead-letter queues require external tooling — Make’s error routes can send notifications, but there is no built-in mechanism to park failed payloads for later replay. Structured logging requires routing execution data to an external service within the scenario itself. Credential management works but is scoped to Make’s internal storage — no external secrets manager integration.
For non-critical workflows — marketing automations, internal notifications, data syncs with tolerance for duplicates — Make’s built-in reliability is sufficient. For mission-critical workflows where failures have financial or compliance consequences, the inability to implement these patterns is a real limitation.
Winner: n8n. The ability to build production-grade resilience patterns — idempotency, DLQ, structured logging, centralized error routing — is what separates a workflow that runs from a workflow that runs in production.
When to Choose Make
Make is the right choice when:
- Visual communication matters. Teams that need to present workflows to non-technical stakeholders benefit from Make’s visual clarity.
- Your team is semi-technical. Comfortable with formulas and logic but not writing JavaScript. Make’s formula syntax is learnable without programming experience.
- Your volume is moderate. Thousands of operations per month but not tens of thousands per day. Make’s pricing stays reasonable in this range.
- You do not need self-hosting. Cloud-only is acceptable, and your industry does not have strict data residency requirements.
An agency building client automations — lead routing, CRM syncs, email sequences — gets tremendous value from Make’s visual editor and template library. That is a legitimate use case, not a consolation prize.
When to Choose n8n
n8n is the right choice when:
- You have technical capacity. A developer or DevOps resource who can manage Docker and write JavaScript/Python within workflows.
- Self-hosting is required. Healthcare, finance, government, or any environment where data cannot leave your infrastructure.
- Your workflows are high-volume. Thousands of executions per day, where per-operation pricing becomes a meaningful cost center.
- Production reliability is non-negotiable. Idempotency, dead-letter queues, structured audit trails, and centralized error routing are requirements, not nice-to-haves.
- Custom code is frequent. More than occasional snippets — real data transformations, API orchestration, or business logic in code.
If three or more apply, n8n is almost certainly the better fit.
Migration Path: Make to n8n
Migrating from Make to n8n is conceptually similar to migrating from Zapier, with one advantage: Make users already think in graphs. The visual mental model transfers well to n8n’s canvas. The migration follows the same five steps:
- Audit active scenarios. Catalog every active Make scenario, its trigger type, module count, and monthly operations.
- Rebuild in n8n. Recreate each scenario as an n8n workflow. Replace Make formulas with JavaScript equivalents. Add error handling and idempotency from day one.
- Parallel run. Run both platforms simultaneously on a subset of traffic to validate output parity.
- Cutover. Disable the Make scenario, route all traffic to n8n, monitor for 48 hours.
- Decommission. Cancel Make once workflows have been stable on n8n for 2+ weeks.
Make users typically find the transition to n8n smoother than Zapier users do, because Make’s mental model — modules, routers, iterators — maps closely to n8n’s nodes, IF nodes, and Loop nodes.
This is exactly what the noorflows Stack Migration covers — a managed migration from Make (or Zapier, or Tray) to n8n, delivered in 7 business days with production-readiness baked in.
What to Do Next
If you are comparing platforms, start with the question that matters most: Does your workflow need to survive failure gracefully? If yes, read the 6-Dimension Production-Readiness Checklist. It will clarify which patterns your workflows need and which platform can deliver them.
If you already have n8n workflows running and want to know where they stand, the noorflows Pre-flight Audit ($247) scores your existing setup against all six production-readiness dimensions and delivers a prioritized report within 24-72 hours.
If you are ready to move from Make to n8n, check the migration cost comparison or email me with a rough count of your active scenarios and monthly operations volume. I will tell you honestly whether the migration makes financial and operational sense for your situation.
If you are looking for industry-specific patterns, see what we build for e-commerce teams, SaaS operations, and digital agencies.