n8n or Make: which automation tool fits your team?

We build client automations in both n8n and Make, and we're often asked which one is better. Neither, on its own. Both are mature visual workflow tools that connect apps, transform data and now run AI steps. The right one depends on who will maintain the workflows, where your data has to live and what shape your volume takes.

This is the difference that surprises people most. n8n charges per execution: a workflow that runs once counts once, whether it has three steps or thirty. Make bills in credits, and most module actions use one credit; since August 2025, some AI and advanced features can use more, depending on factors such as token usage.

So the shape of your workflows matters. A simple automation that runs thousands of times a day with two or three steps can be cheap on either. A long workflow that loops over hundreds of records per run — syncing a product catalogue, processing a batch of invoices — can use a lot of Make credits in a single run, while on n8n it's still one execution. Model a real month of your own workflows on both before you commit.

Both make that easy to try. Make has a free plan, and n8n offers a free trial of its cloud as well as a free, self-hosted Community Edition. Build one real workflow in each — with your actual apps and data — before deciding.

Self-hosting is n8n's clearest advantage. Running it on your own infrastructure keeps customer data, credentials and logs inside your environment, which matters in healthcare, finance and legal work, and for any client with strict data-processing agreements. The trade-off is that you own the uptime, upgrades, backups and security of that server — or you pay someone to.

Make is cloud-only. For many teams that's a feature: no servers to look after and no upgrades to schedule. Check where your plan's data is processed, and whether that fits your obligations, before you build anything that touches personal data.

Team features differ by plan on both. n8n's Business and Enterprise plans add single sign-on, Git-based version control and separate environments for testing and production, which start to matter once several people are building workflows.

Both tools now support AI steps and agent-style workflows, where a model can call tools, read context and decide the next action. n8n's AI nodes let you wire a model to tools, memory and your own data sources with a lot of control, and drop into code when you need to. Make offers AI modules and agents inside its visual builder, which non-developers find easier to follow.

For production agents, we care less about the canvas than about the guardrails around it: validating model output, logging every run and routing uncertain cases to a person. Both tools can do that; n8n's code nodes make the stricter versions easier to build. We explain why that matters in your first AI agent should be boring.

• Integrations: confirm every app you depend on has a maintained module or a usable API, and test authentication, not just that the module exists.
• Error handling: build one failure path end to end — a timeout, a malformed record — and see how each tool reports and retries it.
• Versioning and environments: if you need staging and production copies with reviewable changes, check which plan includes them.
• Ownership after handover: who logs in when something breaks at the weekend, and will that person find the tool readable?

Switching later is a rebuild, not an export: workflows don't move between the two platforms. That's one more reason to model your volumes and your team's skills carefully at the start.

• Choose n8n if developers are involved, you need self-hosting or data residency, you run long or high-volume workflows, or you want custom code in the middle of a flow.
• Choose Make if marketing or operations will build and own the workflows, you want the widest catalogue of ready-made integrations, and your workflows are short and event-driven.
• Use both only with a reason. Two tools means two places to look when something breaks.

Whichever you choose, document each workflow — what triggers it, what it touches and who owns it — and set up error alerts on day one. Our AI automation team builds and maintains workflows on both platforms, and we're happy to model your volumes before you pick.

Written by

Daniel Reyes

Head of AI Automation · 9 years in automation

Daniel leads our AI automation practice, designing the agents and workflows clients run every day — from invoice matching to lead routing. He's an engineer first, and writes here about the unglamorous automations that actually pay back.

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