The short answer: when each tool wins
If you do not want to parse lengthy feature matrices, here is the operational decision rule:
- Pick Zapier if you are a non-technical solopreneur or small marketing team connecting two straightforward SaaS apps (e.g., Google Sheets to Mailchimp) and process fewer than 2,000 tasks per month.
- Pick Make (formerly Integromat) if you need multi-branch routing, array aggregators, and visual debugging across standard cloud apps without managing servers or writing custom JavaScript.
- Pick n8n if you care about data privacy, handle tens of thousands of automated operations, want native AI agent workflows, or need to self-host inside your own VPC or cloud perimeter.
The pricing trap: tasks vs operations vs self-hosting
The fundamental divergence between these platforms is how they bill for automated work.
| Platform | Billing Metric | Economic Consequence |
|---|---|---|
| Zapier | Per task (step in a Zap) | Becomes exponentially expensive as multi-step workflows scale |
| Make | Per operation (each module executed) | Substantially more economical than Zapier, but bills for branching |
| n8n (Cloud) | Per workflow execution | Steps within an execution do not incur extra charges |
| n8n (Self-Hosted) | Free community / fixed enterprise license | Infinite executions capped only by your VPS compute power |
A single workflow that loops over 500 line items will consume 500 tasks in Zapier, rapidly burning through monthly quotas. In n8n, that loop is treated as a single execution, saving hundreds of dollars a month in platform fees.
Why data sovereignty makes n8n the enterprise favorite
Under strict data regulations (GDPR, HIPAA, SOC 2), passing customer data through third-party SaaS middleware creates compliance hurdles. When you use Zapier or Make Cloud, your customer emails, transaction amounts, and internal communications pass through their servers.
With self-hosted n8n:
- The entire application runs inside your AWS, Google Cloud, or Azure private network.
- Sensitive API keys, database credentials, and customer records never leave your infrastructure.
- Zero external data retention agreements are needed beyond the services you directly integrate.
When should you use NONE of them?
Automation platforms excel at glue code: connecting services that already have standard REST APIs. But there are scenarios where visual workflow builders are the wrong architectural choice:
- Sub-second latency requirements — If an automation must complete within 50 milliseconds (e.g., algorithmic trading or synchronous web-app rendering), use custom Node.js, Go, or Python microservices.
- Ultra-high throughput streaming — If you are ingesting millions of telemetry events per minute, dedicated message brokers like Apache Kafka, RabbitMQ, or AWS SQS are built for that workload.
- Complex proprietary algorithmic logic — If a task involves intricate math or custom compiled libraries, writing pure code in version-controlled git repositories is cleaner than massive visual node maps.
Learn how we architect and deploy enterprise pipelines in our Workflow Automation service.