Make vs n8n: Which workflow automation tool is right for you? (2026)

The Real Difference: Integration Breadth vs. Deployment Freedom

Make and n8n solve the same problem-automating repetitive work across your tools-but they target fundamentally different team types. The choice between them isn't really about features on a spreadsheet. It's about whether your team needs to connect to many pre-built integrations (Make) or whether you need complete control over where your automation runs (n8n).

Make excels at answering the question: "Can I connect these two tools without writing code?" With 1,800 integrations, the answer is almost always yes. You're paying for convenience and breadth. n8n asks a different question: "What if I want to run this automation on my own infrastructure?" That matters enormously if you handle sensitive data, need to avoid vendor lock-in, or simply want predictable costs at scale.

The practical difference emerges when you actually build workflows. Make's visual canvas lets anyone drag-and-drop connections between popular apps. If you're connecting Slack to Airtable to Google Sheets to Stripe, Make gets you there fastest. But if you need to self-host because your compliance team won't allow data through Make's servers, or if you run 50,000 workflow executions monthly and need zero marginal cost, Make becomes impossible.

Where Each Tool Wins in Real Scenarios

Make shines for marketing and operations teams with standard SaaS stacks. Imagine a team using HubSpot, Slack, Notion, and Zapier alternatives. Make connects all of them natively. You need a workflow that: captures new CRM leads, posts them to Slack, logs them to Notion, then triggers an email campaign. Make's 1,800 integrations mean someone non-technical can build this in 20 minutes without writing a single line of code. The free tier genuinely works for small teams-you get 1,000 operations monthly, enough for many use cases.

n8n wins for technical teams and enterprises with custom infrastructure. Consider a development team that needs to automate deployments, log events to their custom analytics database, and trigger AI workflows based on production metrics. They might use internal APIs, databases without official integrations, and need the workflow running on their own Kubernetes cluster. n8n's self-hosted model and code nodes let them build this while keeping everything behind their firewall. The cost difference is stark: self-hosting n8n is free, while Make's pricing climbs with every operation.

Pricing: What You Actually Pay Per Use Case

Make uses operation-based pricing. Each action in your workflow-sending a Slack message, creating a row in a database, calling the OpenAI API-costs operations. A moderately complex workflow might use 100-200 operations per execution. If you run it 1,000 times monthly, you've used 100,000-200,000 operations. That puts you in paid tiers quickly, potentially $300-500 monthly for workflows that would have cost nothing on n8n.

n8n's self-hosted version has no per-execution costs. You pay for your server (a $5 VPS works fine for many use cases). This flips the math entirely. A team running thousands of daily automations pays the same $5 monthly they always did. The trade-off: someone needs to manage Docker, updates, and troubleshooting.

n8n's cloud offering bridges the gap-it's priced per workflow execution, similar to Make-but it's positioned for teams that want managed hosting without self-hosting complexity. For teams genuinely considering it, though, self-hosting is the real advantage.

One Specific Scenario for Each

A 12-person SaaS company's marketing team: They use Hubspot, Segment, Slack, Intercom, and Notion. New leads must sync across all systems, with notifications in Slack. They have zero engineers available. Make is the right call. The 1,800 integrations mean marketing ops can build and maintain everything. Cost is predictable and low for their workflow volume. Self-hosting would waste precious engineering time.

A healthcare startup processing patient data: They need to automate workflows that touch patient records, but compliance requires data never leaves their infrastructure. They can self-host n8n on a private AWS VPC. They write a JavaScript code node to anonymize certain fields before any external API call. They trigger everything from internal webhooks. Make isn't an option here-not due to missing features, but due to architecture requirements. n8n's self-hosted model is the only choice.