Zapier vs Make vs n8n: How to Choose a Workflow Automation Platform

Zapier, Make, and n8n all connect apps and move data between them without custom code. On a feature checklist they look similar. In practice they suit quite different teams, and the wrong choice usually shows up a few months later as a surprising bill, a workflow that is painful to maintain, or a security review that the tool cannot pass.

This comparison focuses on the differences that actually decide the choice: how each one charges, how complex logic is built, where your data runs, and who on your team will maintain the workflows. Pricing tiers change often, so check each vendor's current pricing page before committing; the billing models described here are more stable than the numbers.

The short version

Pricing models: where most surprises come from

The three tools measure usage in fundamentally different ways, and that difference matters far more than the headline monthly price.

Zapier: tasks

According to Zapier's own help documentation, a task is any successful action step. Triggers do not use tasks, and neither do filters, paths, or its built-in Formatter steps. So a Zap that triggers on a new form submission, then creates a CRM contact and sends a Slack message, uses two tasks per run.

This model is easy to predict for short workflows. It gets expensive when a workflow has many action steps or processes items in a loop, because each action on each item counts.

Make: credits (formerly operations)

Make historically billed by operations, where most module runs counted as one. In August 2025 it moved to credits, converting operations at a 1:1 ratio for standard modules, while some AI modules and other features consume credits based on factors such as tokens or file size. In a workflow that loops over 50 rows and runs four modules on each, those module runs add up quickly.

Make tends to be cheaper than Zapier for complex, multi-step workflows at moderate volume, but you need to watch loops and polling triggers, which can consume credits even when nothing new has happened.

n8n: executions

n8n Cloud charges per workflow execution. A workflow with three nodes and one with forty count the same: one execution each time it runs. The self-hosted Community Edition is free to run on your own server under n8n's Sustainable Use License, a source-available "fair-code" license rather than a traditional open-source one. Read the license if you plan to embed n8n in a product you sell.

For long, complex workflows running at high volume, this model is usually the most economical. The cost shifts from subscription fees to engineering time and server maintenance if you self-host.

A worked example

Take a workflow that runs 2,000 times a month. Each run receives an order webhook, looks up the customer, loops over an average of three line items to update inventory, and posts a summary to a chat channel.

• Zapier: roughly 1 lookup + 3 inventory updates + 1 message = about 5 tasks per run, so around 10,000 tasks a month.

• Make: a similar count of module runs, plus the trigger and iterator, so somewhere above 10,000 credits a month.

• n8n Cloud: 2,000 executions a month, regardless of the number of steps.

The exact figures depend on how you build the workflow, but the pattern holds: the more steps per run, the more n8n's model favours you, and the simpler the workflow, the less the difference matters.

Building complex logic

Zapier is designed around a straight line of steps. Paths allow branching, and there are tools for loops and lookups, but workflows with many branches become hard to read and edit. It shines when the logic is "when X happens, do Y and Z".

Make shows workflows as a visual map, with routers for branching, iterators for splitting arrays into items, and aggregators for combining them back. Data mapping between modules is precise. Operations staff who are comfortable with spreadsheets usually pick it up within a few days, and complex flows remain readable.

n8n also uses a node canvas but expects more technical comfort. You can drop into a Code node to transform data with JavaScript or Python, call any HTTP API directly, and build sub-workflows. For developers this is liberating. For a marketing coordinator maintaining the workflow alone, it can be daunting.

Integrations

Zapier has the largest catalogue of pre-built app connections, which matters if you rely on niche SaaS tools. Make covers most mainstream business apps and offers a solid HTTP module for the rest. n8n's catalogue is smaller, but its HTTP Request node and the ability to write code mean almost any API can be reached by someone technical.

Before deciding, list the five or six apps your workflows will touch and check each platform for a native integration that supports the specific triggers and actions you need. "Has an integration" and "supports the action I need" are not the same thing. The same principle applies to any software purchase; the guide on how to evaluate a SaaS tool before buying covers integration depth in more detail.

Data location, security, and compliance

Zapier and Make run your workflows in their cloud. Data passes through their infrastructure, and you rely on their security controls and certifications. For most small businesses this is fine.

n8n can run entirely on your own server or private cloud. That matters if you process sensitive data, have customers who require data to stay within a particular region or network, or need workflows to reach internal systems that are not exposed to the internet. The trade-off is responsibility: updates, backups, uptime, and securing the instance become your job.

AI features

All three now offer ways to call large language models inside workflows and to build simple AI agents. The practical difference is cost and control. Platform-native AI steps are convenient but may bill differently from ordinary steps (Make's credit model explicitly varies for some AI modules). Connecting directly to a model provider's API with your own key gives you clearer cost visibility. Whichever tool you choose, keep a human review step before AI-generated output reaches customers.

How to choose

Work through these questions in order:

1. Who will build and maintain the workflows? If it is non-technical staff with no developer support, start with Zapier. If it is an operations person who enjoys building systems, Make. If it is a developer, n8n.

2. Must data stay on your own infrastructure? If yes, n8n self-hosted is the only option of the three.

3. How many steps per run, and how many runs per month? Estimate both for your three most important workflows and price them on each platform's current plans.

4. Are your apps supported with the specific actions you need? Check before you build.

5. What happens when something breaks? Look at error handling, retry options, and alerting. A workflow that fails silently is worse than no automation.

Common mistakes

• Choosing on the free plan. Free tiers hide the cost structure you will live with at real volume.

• Automating a broken process. If the manual process is unclear, automation just makes the confusion faster. Map the process first.

• No owner. Every workflow needs a named person who gets the failure alerts.

• Ignoring polling intervals. Some triggers check for new data on a schedule. Frequent polling can burn usage or delay workflows.

• Underestimating self-hosting. "Free" n8n still needs a server, monitoring, and someone to apply updates.

Where to go from here

Pick your single most valuable workflow, build it on the platform that fits your team, and run it for a month while tracking cost and failures. That trial will tell you more than any comparison table. For a broader look at which manual processes are worth automating in the first place, see the overview of how businesses are moving from manual workflows to automated systems.