Most businesses that start automating with AI end up on one of three platforms: Zapier, Make or n8n. All three connect your apps, all three now ship AI agents, and all three will happily run a demo workflow in an afternoon. The differences only show up later, in the monthly bill, in what happens when a step fails, and in where your customer data travels. This is how we compare them before recommending one, and the questions that usually settle it.
The short answer
Choose Zapier when the people building automations are not technical, the workflows are short, and the apps you use are niche enough that only the largest integration library covers them. Choose Make when workflows branch, loop and transform data, and you want a visual builder that stays affordable at volume. Choose n8n when workflows are long, AI-heavy or handle sensitive data, and you have someone who can own a technical tool, or a partner who will.
Many of our clients end up with two: Zapier or Make for quick team-built automations, and n8n for the core processes that the business depends on.
- Zapier: easiest to learn, largest app library, most expensive per step
- Make: visual and flexible, low cost per operation, steeper learning curve
- n8n: priced per workflow run, self-hostable, best suited to complex AI workflows
How each platform bills, and why it matters more than the plan price
The headline plan prices are close enough that they rarely decide anything. What decides the bill is the unit each platform meters. Zapier counts tasks: every successful action step is one task, while triggers and filters are free. Make counts credits: every module run is one credit, including a trigger that checks for new data and finds none. n8n counts executions: one run of a whole workflow is one execution, whether it has three steps or fifty.
That difference compounds quickly with AI. A lead-handling workflow that enriches a contact, asks a model to classify it, writes to the CRM, posts to Slack and sends an email is five billable steps on Zapier, roughly six credits on Make and a single execution on n8n. Multiply by a few thousand leads a month and the gap becomes the main cost of the project.
At the time of writing, Zapier's paid plans start at roughly $20 to $30 a month for 750 tasks, Make's Core plan starts at about $9 a month for 10,000 credits, and n8n Cloud starts at €24 a month for 2,500 executions. The n8n Community Edition is free to self-host with unlimited executions, so you pay only for the server. Prices change often, so check each vendor's pricing page before you commit.
- Zapier: per successful action step (tasks)
- Make: per module run, including empty trigger checks (credits)
- n8n: per complete workflow run (executions), or free when self-hosted
AI agents on each platform
All three now let a language model decide which step to take next rather than following a fixed path. Zapier Agents are built by describing the job in plain English and can act across its library of more than 8,000 apps, which makes them the quickest to get running. Make's AI Agents sit inside its visual canvas, and a reasoning panel shows each decision the agent made, which helps when you need to explain a result.
n8n goes furthest for custom work. Its AI Agent node is built on LangChain, supports memory that persists across runs, can call any HTTP API or your own code as a tool, and can use a model you host yourself. That flexibility is why we use it for agents that read a knowledge base, qualify leads or draft replies, as described in our article on AI agents that qualify leads.
Whichever platform you pick, the model is rarely the weak point. Scope, the quality of the data the agent can read and the rules for when it hands over to a person decide whether it works.
Integrations: breadth versus depth
Zapier's library is the largest by a wide margin, and for long-tail tools it is often the only platform with a ready-made connector. Make covers around 3,000 apps, and its connectors tend to expose more of each app's API. n8n has fewer native nodes, but its HTTP Request node and code steps mean any tool with an API can be connected, which in practice covers almost every CRM, ad platform and database our clients use.
Before choosing, list every app the workflow must touch and check the specific triggers and actions you need, not just the app's name. A connector that exists but cannot do the one action you need is the most common reason a build moves platforms halfway through.
Data control and compliance
Zapier and Make are hosted only, so every record a workflow processes passes through their infrastructure. For most marketing and sales automation that is fine, and both publish their security and data-processing terms.
n8n can run on your own server or in your own cloud account, so customer data, prompts and model responses never have to leave your environment. For clinics, finance, legal and any business with strict data residency rules, that option alone often decides the platform. It does mean someone has to own updates, backups and monitoring.
Error handling and maintenance
Automations fail quietly. An API changes, a token expires or a model returns something unexpected, and leads stop flowing without anyone noticing. All three platforms can retry steps and send alerts, but they differ in how much control you get. Make and n8n both support dedicated error routes, so a failed step can log the problem, notify someone and continue. n8n adds error workflows that catch failures across every workflow in one place.
Whatever you choose, build monitoring in from day one: an alert channel, a log of failed runs and a weekly check that the volume going through each workflow looks normal.
- Name every workflow after the business process it runs
- Send failures to a channel a person actually reads
- Keep credentials in the platform's credential store, never in steps
- Document the trigger, owner and purpose of each workflow
The questions we ask before choosing
We pick the platform last, after we understand the work. These questions settle it for most clients.
- Who will build and maintain the workflows after launch?
- How many runs per month, and how many steps per run?
- Does any workflow touch health, financial or other sensitive data?
- Which apps, and which exact triggers and actions, are required?
- Will AI make decisions in the workflow, or only generate text?
- What happens to the business if a workflow stops for a day?
Our recommendation
If you are starting out and want a few time-saving automations your team can build themselves, start with Zapier or Make. If AI is becoming part of how leads are handled, how customers are answered or how work moves between systems, build those core processes on n8n, where cost does not grow with every step and you keep control of the data.
The platform matters less than the process behind it. A clean CRM, clear ownership and well-defined triggers will make any of the three work, which is why we audit the process before building the automation, as covered in our article on the CRM audit we run before automating anything.