Every automation project starts with the same question: which platform do we build on? It sounds like a tooling decision. It's actually a decision about cost structure, maintenance, and who in your company can safely touch the thing once it's live.
We deploy on all three of these platforms — and replace all three with custom builds when they run out of road — so this is not a vendor pitch. It's the decision framework we use on real client work.
The short version
- Zapier gets a non-technical team to its first working automation fastest, and costs the most once volume grows.
- Make sits in the middle: a visual canvas with real branching and a pricing model that stays reasonable at moderate volume.
- n8n offers the most power and control — code steps, self-hosting, flat execution pricing — at the price of a steeper learning curve.
- None of them is the answer once your logic needs versioning, testing, or sub-second performance. That's custom territory.
What each platform actually is
Zapier is the household name: 7,000+ app connectors and a linear trigger-action model. Its genius is that a marketing manager can wire a form to a spreadsheet in ten minutes without asking anyone for help. Its ceiling is that the linear model fights you the moment a workflow needs loops, branches, or non-trivial data transformation.
Make (formerly Integromat) is a visual canvas. Workflows are node graphs with routers, iterators, and aggregators, so genuinely complex flows are buildable without code. The trade is readability: a fifty-module Make scenario is powerful, and also the kind of artifact only its author fully understands.
n8n is the developer-grade option: source-available, self-hostable, with first-class code nodes and native AI-agent tooling. You can run it on your own infrastructure, keep customer data inside your own perimeter, and drop into JavaScript whenever a connector falls short.
Head to head
| Zapier | Make | n8n | |
|---|---|---|---|
| Pricing unit | Per task (each step, each record) | Per operation | Per execution (or flat, self-hosted) |
| Learning curve | Minutes | Hours | Days |
| Self-hosting | No | No | Yes |
| Complex logic | Limited | Good | Excellent — code when needed |
| Error handling | Basic replay | Scenario-level handlers | Full retry / branch / alert control |
| Best for | First automations, small teams | Visual builders, mid volume | Engineering-minded teams, data control |
Pricing: where the bills diverge
The pricing models matter more than the sticker prices. Zapier bills per task — every step, for every record. Make bills per operation, which behaves similarly but at a lower unit price. n8n bills per execution: one workflow run counts once no matter how many steps it contains, and a self-hosted instance has no per-run metering at all — you pay for the server.
Run the numbers on a real workload and the divergence is dramatic. Say you enrich 10,000 leads a month through a six-step workflow. On Zapier that's 60,000 tasks — a mid-tier plan blown through before month's end. On Make it's 60,000 operations at a friendlier rate. On n8n it's 10,000 executions on a cloud plan, or effectively a fixed server bill if you self-host. Same workflow, order-of-magnitude different cost curve.
This is why "which tool costs least?" has no static answer. At ten runs a day, tooling cost is noise and Zapier's speed wins. At ten thousand, the platform choice is the difference between a rounding error and a five-figure annual line item.
The 2026 wrinkle: AI inside the workflow
All three platforms now ship AI features, and the depth varies. Zapier's AI steps are great at the small stuff — drafting a reply, classifying a ticket — inside its linear model. Make lets you weave model calls through complex scenario logic. n8n goes furthest: native agent nodes with tool-calling, memory, and model routing, which is why it has become the default orchestration layer for teams building agentic workflows rather than sprinkling AI on top of old ones. If your roadmap includes agents that decide and act — not just generate text — that gap should weigh heavily.
The deciding factor nobody prices in: maintenance
Automations are not fire-and-forget. APIs change, edge cases arrive, and the person who built the workflow leaves. The honest question is: who fixes this at 9am on a Tuesday when it breaks? If the answer is a non-technical operator, Zapier's simplicity is worth its premium. If you have (or hire) engineering support, n8n's inspectability — versionable JSON, code steps, real logs — makes failures diagnosable instead of mysterious.
When none of them is the answer
All three platforms share a ceiling. When a workflow needs deterministic pricing logic, heavy data processing, sub-second response times, or an audit trail a regulator would accept, gluing modules together stops being engineering and starts being hope. That's the point where we move clients to a custom logic engine — real code, tested and versioned — usually still orchestrated by n8n at the edges.
What we actually deploy
Our default is n8n, self-hosted, for the flat cost curve, the data control, and the AI-agent support — it's the orchestration layer under most of our client stacks and our own Scalaro sales system. We reach for Make when a client's own team will co-own a visually complex workflow, and we leave Zapier in place when it's already working and volume is low — ripping out a functioning automation to save a small subscription is bad math.
The tool matters less than the map. Pick the workflow with the highest hours-saved first, choose the platform that fits your volume and your team, and let the second workflow inherit the infrastructure of the first.
