Zapier vs Make vs n8n: Automation Platform Comparison

Workflow automation has moved from a nice-to-have to a core part of how businesses operate. Zapier, Make (formerly Integromat), and n8n represent three distinct approaches to the same problem: connecting apps and automating repetitive work. Zapier prioritizes simplicity and breadth of integrations. Make offers visual workflow building with more complexity at a lower price. n8n provides a self-hostable, open-source option with full control over your data and infrastructure.

Each platform serves a different buyer profile. This comparison examines where each tool excels and where it falls short based on building and maintaining production automations across all three.

Feature Comparison

Pricing

The pricing difference between these platforms is substantial. Make offers roughly 10x more operations per dollar compared to Zapier. n8n's self-hosted option eliminates per-execution costs entirely — you pay only for your own infrastructure. For teams running thousands of automations, the cost savings from switching away from Zapier can be significant.

Zapier's pricing model charges per task, where each action in a multi-step workflow counts as a separate task. A 5-step automation that runs 100 times uses 500 tasks. Make counts operations similarly, but the per-operation cost is much lower. n8n's cloud pricing is based on executions rather than individual steps, which is more predictable for complex workflows.

Ease of Use

Zapier is the easiest to learn. Its linear, step-by-step editor walks you through trigger selection, app connection, and action configuration. The interface hides complexity by default. For simple automations — "when a form is submitted, add a row to a spreadsheet and send a Slack message" — Zapier gets you there faster than either alternative. The AI-powered natural language builder can create basic automations from a plain English description.

Make uses a visual canvas where you drag modules and connect them with lines. This interface is more powerful but requires more upfront learning. Understanding how data flows between modules, how iterators and aggregators work, and how to configure routers takes time. Once you understand Make's paradigm, building complex workflows is faster than in Zapier because you can see the entire flow at once.

n8n's interface is similar to Make's — a visual canvas with nodes and connections. The learning curve is comparable, with the added complexity of self-hosting setup if you choose that route. n8n's code nodes allow writing JavaScript or Python directly within workflows, which is powerful for technical users but adds another dimension of complexity.

Integration Breadth and Depth

Zapier's library of 7,000+ app integrations is unmatched. If an app has an API, Zapier probably has an integration. This breadth is Zapier's strongest competitive advantage. For teams that use niche or industry-specific tools, Zapier is often the only option that connects everything without custom development.

Make supports around 1,800 apps, which covers most popular SaaS tools. The integrations tend to be deeper than Zapier's, exposing more API endpoints and configuration options per app. Where Zapier might offer 5 actions for an app, Make might offer 15.

n8n has around 400 built-in integrations, significantly fewer than Zapier or Make. However, the HTTP Request node and the ability to write custom code mean that any API-accessible service can be integrated. For technical teams comfortable with API calls, the smaller built-in library is not a limitation.

Complex Workflow Capabilities

This is where Zapier falls behind. While Zapier supports paths (conditional branching) and multi-step workflows, the linear editor becomes awkward for workflows with multiple branches, loops, or error handling paths. Complex logic is possible but difficult to visualize and maintain.

Make handles complexity well. Routers split execution into parallel paths. Iterators process arrays item by item. Aggregators collect results back together. Error handlers can be attached to any module. The visual canvas makes it possible to build and understand workflows with dozens of steps and multiple branches.

n8n matches Make's capabilities for complex workflows and adds the ability to embed code at any point. The Merge node handles data combination from parallel paths. Sub-workflows enable modular design. For the most demanding automation use cases — data pipelines, multi-system synchronization, complex business logic — n8n offers the most flexibility.

Data Privacy and Self-Hosting

n8n's self-hosting option is unique among these three platforms. Organizations that cannot send data through third-party servers — due to compliance requirements, data residency laws, or internal security policies — can run n8n on their own infrastructure. Your workflow data, credentials, and execution logs never leave your environment.

Zapier and Make are cloud-only. Both comply with SOC 2, GDPR, and other security standards. Both encrypt data in transit and at rest. For most businesses, this is sufficient. But for highly regulated industries or organizations with strict data sovereignty requirements, the lack of a self-hosted option is a real limitation.

Reliability and Support

Zapier has the most mature infrastructure, with published uptime SLAs and a proven track record processing billions of tasks. Support is responsive, with documentation covering virtually every integration. The community forum and template library are extensive.

Make's reliability has improved steadily. Support quality varies by plan tier. Documentation is good but not as comprehensive as Zapier's. The community is smaller but active, particularly in European markets where Make has a strong presence.

n8n's reliability depends on your deployment (self-hosted) or their cloud infrastructure. Self-hosted n8n requires your own monitoring, backups, and maintenance. The trade-off for control is operational responsibility. n8n's community is strong in developer-oriented forums, and the open-source nature means you can inspect and fix issues at the code level.