n8n vs. Zapier vs. Make: Which Tool Fits?

The short answer

Zapier, if you want to connect two services quickly and need the largest selection of ready-made integrations. Make, if your processes need to branch, work through lists, and respond to errors in a differentiated way. n8n, if data can't leave your own environment, volume is high, or you want to bring in your own code and self-hosted AI models.

These three sentences answer the question in four out of five cases. The rest of this article explains why — and where the rule of thumb doesn't hold.

The three tools head-to-head

Cost: three completely different logics

The most common mistake when comparing is laying the monthly base prices side by side. What matters is the billing unit, because it grows at a different rate with your volume.

Zapier counts actions. A process with three actions costs three units per run. Filters that stop the run cost nothing. Costs rise linearly with the number of runs — predictable, but noticeable at high throughput.

Make counts module times record. Here usage multiplies: a scenario with four modules and ten records costs many times more than a single run, because every module processes every record separately. At small data volumes, Make is often cheaper than Zapier; at large lists it can get more expensive — unless you filter early and aggregate before writing.

Self-hosted n8n counts nothing at all. You pay server costs and operating effort, but no per-run fee. That's the decisive difference: past a certain volume, n8n isn't a bit cheaper, it's cheaper by orders of magnitude. The hosted version, on the other hand, bills by executions — where a workflow with twenty steps counts as one execution, which plays out very differently for extensive processes than counting per step.

The practical consequence: don't calculate in euros per month, but in units per typical run, multiplied by your realistic monthly volume. At a few hundred runs, the tool choice is irrelevant cost-wise. Past several tens of thousands, it decides a three-figure amount every month.

Complexity: what each tool can actually do

Zapier is built for linear processes: a trigger, a few actions, optionally one branch. That covers most everyday tasks. As soon as you need to loop through a list, merge records, or restructure nested data, it gets unwieldy to impossible. Details on that are in the Zapier Guide.

Make is built for exactly that. Iterator and aggregator break down and bundle records, routers split the process into parallel branches, and every module can have its own error path attached. The scenario stays readable as a diagram throughout — an underrated advantage when a colleague has to understand the process a year from now. More in the Make Guide.

n8n treats lists of records as the normal case, not the special case: a node processes every incoming element by default. On top of that comes the code building block, which can solve any transformation for which no ready-made module exists. The price for that is the steepest learning curve of the three — see the n8n Guide.

Hosting and data privacy

This is where the hardest dividing line runs, and it's not a matter of taste.

Zapier and Make are purely cloud services: your data is processed at the provider. For many use cases, that's acceptable with the right contractual basis. But once especially sensitive data is involved — health data, applicant data, extensive customer records — or internal systems need to be connected that aren't reachable from outside the company network at all, self-hosting is the only option. Of the three, only n8n can do that.

Anyone who additionally wants to avoid content going to an external model provider combines self-hosting with a locally run language model; see Using Local LLMs & Open-Source Models for details. This combination is why n8n shows up so often in regulated industries.

AI features compared

All three can now integrate language models into workflows — the differences lie in the depth.

Zapier and Make offer AI building blocks that call a model and pass on the result, plus features for chatbots and agentic processes with access to connected services. That's completely sufficient for summaries, classification, and text drafts.

n8n goes further: interchangeable model providers including self-hosted models, building blocks for vector stores and knowledge retrieval, and agents with tool access. When AI isn't a side step but the core of the process, the gap becomes clear.

The same rule applies to all three: an AI step needs a validated output structure, closed selection lists at decision points, and hard cost limits. Which patterns work when is something AI Automation sorts out.

Decision matrix

And the honest bonus recommendation: the decision isn't final. Many teams start with Zapier, later move data-heavy processes to Make or n8n, and keep Zapier for the simple connections. Running tools in parallel isn't a failure — it's often the cheapest setup.

The three most common wrong decisions

Choosing by base price. The monthly plan tells you nothing as long as you don't know how many billing units your typical process consumes. Run the numbers on a real workflow before you commit.

Starting too powerful. A self-hosted n8n for three simple notifications means server maintenance, updates, and backups for a benefit that a click-tool delivers in twenty minutes. Match the complexity to what your process actually needs.

Checking data privacy as an afterthought. The hosting question can't be retrofitted without moving the entire setup. Before your first workflow, clarify what kinds of data will flow through the tool — that decides the choice faster than any feature argument.

Conclusion

The three tools target different needs, not different price classes: Zapier for speed and breadth of integrations, Make for branching and data-heavy processes, n8n for control over hosting, code, and models. The choice comes down to three questions: how complex is your data, how high is your volume, and is that data allowed to leave the building? Anyone with those three answers no longer needs a feature comparison.