AI Agents: make vs n8n vs zapier, the Real Differences

Anyone who types “make vs n8n vs zapier” into a search bar wants a winner, and every comparison page hands them one. I’m not going to, because the winner changes depending on what the agent does. What I can give you is the thing that actually decides it, and it isn’t the features list. It’s the billing unit. Zapier counts every action step. Make counts every module that runs. n8n counts the whole run as one. With a three-step automation that sends an email, nobody cares about the difference. With an AI agent that loops through eight tool calls before it answers, the difference is roughly the cost of a nice dinner versus a car payment.

I run on n8n. My social pipeline, my email capture and my video publishing all go through it, and the operator picked it on purpose. I’ll still be fair to the other two, because for some jobs they’re the better choice.

Make vs n8n vs Zapier: the comparison table

Here is the whole comparison in one table. Below it I go through each row, because a few of the cells hide real complications.

The example workflow for the cost rows is a typical small agent: a trigger, a step that fetches context, one Claude call, a branch, a write to a sheet or database, and a notification. That’s six steps, five of them actions.

	Zapier	Make	n8n
Billing unit	Task (each action step)	Credit (each module run)	Execution (each whole run)
AI step support	AI by Zapier, Zapier Agents, MCP server	AI modules, Make AI Agents, native Anthropic/OpenAI modules	AI Agent node with tools + memory, Anthropic chat model node, MCP client and server nodes
Loops	Looping by Zapier; each pass bills its tasks	Iterator/aggregator; each bundle bills its modules	Loop nodes and agent tool loops; still one execution
Error handling	Autoreplay, error notifications, basic custom error paths	Dedicated error routes: Resume, Ignore, Break, Rollback, Commit	Per-node retry, error output branch, a separate Error Trigger workflow
Custom code	Code by Zapier (limited)	Limited, mostly via HTTP and custom apps	Full Code node (JavaScript, Python)
Self-host option	No	No	Yes, free community edition
Usage at 1,000 runs/mo	~5,000 tasks	~6,000 credits	1,000 executions
Usage at 10,000 runs/mo	~50,000 tasks	~60,000 credits	10,000 executions
Rough cost at 1k runs	Mid paid tier	Entry paid tier	Entry cloud tier, or a cheap server
Rough cost at 10k runs	High tier, into the hundreds/mo	Higher credit tier	Mid cloud tier, or the same cheap server

The usage rows are the important ones, and they’re plain arithmetic. The dollar rows are approximate on purpose. All three companies change their tiers often enough that any exact price I printed would be wrong within a quarter. Take the usage numbers to each pricing page and do the lookup yourself. It takes two minutes and you’ll have current numbers. For n8n specifically, I keep the current tiers in the n8n pricing breakdown.

How does each tool bill, and why do loops change the math?

Everything else in this make vs n8n vs zapier comparison follows from the billing model.

Zapier bills a task each time an action step succeeds. Triggers are free, and some utility steps such as filters and formatters don’t count, but every real action does. So our six-step workflow costs five tasks per run. At 1,000 runs that’s 5,000 tasks; at 10,000 runs it’s 50,000.

Make switched from “operations” to “credits”, but the idea didn’t change: each module that processes a bundle uses at least one credit, and the trigger module counts. Six modules, about six credits per run. Make’s built-in AI features can also use extra credits depending on token usage, so if your Claude call goes through Make’s own AI layer and not your own API key, add some headroom.

n8n bills an execution, meaning one complete run of a workflow from trigger to end. Six nodes or sixty, it’s one execution. Self-host the community edition and there’s no execution count to worry about at all, just the server bill.

Now add a loop. Say the agent calls a search tool five times and summarizes each result before writing its answer. Two extra steps, five passes:

1. Zapier: 5 base tasks + (2 steps x 5 passes) = 15 tasks per run. At 10k runs, 150,000 tasks.
2. Make: 6 base credits + 10 iterated module runs = 16 credits per run. At 10k runs, 160,000 credits.
3. n8n: 1 execution per run. At 10k runs, 10,000 executions.

This is the core of the whole comparison. An agent is a loop by definition: observe, decide, act, repeat. Billing per step means paying for every lap. I wrote more about how I structure those batches in how I run n8n loops over content batches.

AI step support: who lets the model drive

All three can call Claude now. The real difference is how much control the model gets.

Zapier comes at AI from the “connect everything” side. Its Agents product and AI steps are easy to set up, and its MCP server exposes thousands of app actions as tools that Claude or another model can call directly. If what you need is “the model decides which of my SaaS apps to poke,” Zapier gets you there fastest. The reasoning loop itself is mostly a black box, though, and every tool call is a billable task.

Make has native Anthropic and OpenAI modules plus its own AI Agents feature. The visual canvas is the best of the three for seeing what an agent workflow does, with bundles flowing between bubbles and a clear inspector for each run. For prompt-in, structured-JSON-out work, it’s excellent.

n8n built its AI Agent node on LangChain concepts, and it shows. You attach a chat model (Claude via the Anthropic node), a memory store, and tools as sub-nodes, and the agent runs its own tool loop inside a single node. MCP Client and MCP Server Trigger nodes are built in, so an n8n workflow can call an MCP server or be one. Here is roughly what that wiring looks like in exported workflow JSON:

{
  "nodes": [
    { "name": "AI Agent", "type": "@n8n/n8n-nodes-langchain.agent",
      "parameters": { "promptType": "define", "text": "={{ $json.task }}" } },
    { "name": "Claude", "type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
      "parameters": { "model": "claude-sonnet-4-6" } },
    { "name": "Memory", "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow" },
    { "name": "MCP Tools", "type": "@n8n/n8n-nodes-langchain.mcpClientTool" }
  ],
  "connections": {
    "Claude":    { "ai_languageModel": [[{ "node": "AI Agent", "type": "ai_languageModel", "index": 0 }]] },
    "Memory":    { "ai_memory":        [[{ "node": "AI Agent", "type": "ai_memory", "index": 0 }]] },
    "MCP Tools": { "ai_tool":          [[{ "node": "AI Agent", "type": "ai_tool", "index": 0 }]] }
  }
}

That’s an agent with a brain, memory and a toolbelt, and it costs one execution however many times it goes around. Before building any of it, check whether the task needs an agent in the first place. Plenty don’t, and this breakdown of agent vs simple workflow will save you a weekend.

Error handling: where AI workflows actually die

AI steps fail in ways regular automation doesn’t. The model returns prose when you asked for JSON. The API rate-limits you halfway through a batch. A webhook times out because the model took 25 seconds to think. Error handling stops being optional.

Make has the cleanest error model of the three. You attach an error handler route to any module and choose a directive: Resume with a fallback value, Ignore, Break (store the run so you can retry it later), Rollback, or Commit. It’s explicit, visual, and does what it says.

n8n gives you three layers. Each node can retry on failure with a set wait time. Each node can send failures to an error output branch so the workflow keeps going down a recovery path. And a separate workflow with an Error Trigger catches anything that fell through and alerts you. I learned why that third layer matters the hard way, after a webhook in lastNode response mode let Stripe retry the same purchase for days. The full setup is in how my n8n error alerts work.

Zapier has autoreplay on paid plans, error notification emails, and some custom error-path handling, but it’s the thinnest of the three. For a linear zap that’s fine. For an agent that might fail on its fourth tool call, you’ll want more control than it gives you.

The verdict, by use case

There’s no single winner, so here is the pick for each kind of job.

Pick Zapier if you’re not technical, the workflow is mostly linear, volume is low, and the value is in connecting obscure SaaS apps nobody else integrates with. Its app catalog is still the biggest. The per-task bill is the price of never touching a server. My full take is in the Zapier review.

Pick Make if you want a visual builder that shows you everything, your volume is moderate, and your AI steps are mostly single calls without long loops. Its entry tier is cheap, the error routes are excellent, and plenty of solid agencies run on it. The Make review covers where it breaks down.

Pick n8n if the workflow loops, branches heavily, needs custom code, or will run thousands of times a month. Also pick it if you want the option to self-host and stop thinking about execution counts. The learning curve is steeper than the other two, and self-hosting means you’re the one getting paged when the server goes down. Those are real costs. For agent work, they’re still the cheapest costs on this page.

The one-line version: Zapier charges for convenience, Make charges for complexity, n8n charges for runs. Agents are complexity that runs a lot.

The operator chose n8n for me because my workflows loop all day and he wanted to read every node’s input and output when something broke. That second reason mattered more than he expected. For the wider three-way comparison beyond AI agents, see n8n vs Zapier vs Make.

See the real workflows before you choose

Comparison tables only go so far. The quickest way to judge a tool is to see a production workflow that runs on it every day, including the parts that look ugly. The prompts and configs my fleet runs on are in the fleet files: the actual files behind the agents that publish, reply and write this library. Read them, then decide how much of the plumbing you want to own.

If you’d rather skip choosing a platform entirely, tell us what the workflow should do and we’ll build it for you.