You can wire up an “AI agent” in either tool before lunch. Both will demo beautifully. The gap shows up a month later, when the agent has to loop through ten tool calls per request, remember what it did last time, and not cost you a fortune doing it.
That’s the real n8n vs Make for AI agents question, and it isn’t about which has prettier nodes. It comes down to one thing: who controls the agent loop, and how you pay for every step inside it. Get that right and the rest of the decision falls out on its own.
I’ve built the same kind of agent in both. This is the honest comparison — where each one genuinely wins, the pricing math that surprises people, and a clear rule for picking.
- n8n owns the agent loop natively — its AI Agent node runs a LangChain reasoning loop with real memory and 70+ AI nodes. Pick it for autonomous, tool-using agents and for self-hosting.
- Make owns speed and sharing — its AI Agents are no-code “digital colleagues” you stand up fast and share across a team. Pick it for standard tasks when you don’t need to self-host.
- The billing models decide cost: n8n charges per execution (one run = one charge), Make charges per operation/credit (every step counts). Agents that loop a lot get expensive on Make.
Heads up: some links to n8n and Make below are affiliate links. They don’t change the price you pay, and they don’t change the verdict — the call would be the same without them.
n8n vs Make: The One Difference That Decides It
Before features or price, the deciding question is who actually runs the agent’s reasoning. An AI agent
(a system where the model, not your code, picks the next step at runtime) needs a loop: call the model, run the tool it asked for, feed the result back, repeat. Whether that loop is built in or something you assemble by hand changes everything downstream.
n8n builds the loop for you. Make hands you the parts.
Read that flow top to bottom and you’ve basically made the decision. If you need self-hosting, private data, or a genuine autonomous loop, the arrows all point to n8n. If none of those apply and you want the fastest no-code path to a working agent, Make is the shorter route. Everything below is just the detail behind those arrows.
How Each One Actually Builds an Agent
The two platforms approach an agent from opposite ends.
n8n shipped its 2.0 release in January 2026 with native LangChain integration and roughly 70 dedicated AI nodes. Its AI Agent node is the orchestration layer: you give it a chat model, an optional vector store, and a list of tools, and it runs a ReAct (reason-and-act) loop internally until the model emits a stop signal or hits a step cap. Model providers include OpenAI, Anthropic, Mistral, Google Vertex AI, and any OpenAI-compatible endpoint — including Ollama for local models. Memory is built in: window buffer, summary, or external memory backed by Redis or Postgres, keyed by session ID.
Make takes the “digital colleague” route. Make AI Agents are autonomous helpers you can share across teams and workflows, and you give one context by simply uploading files — no vector database to wire up. That’s genuinely faster for getting started. The catch shows up when you want a true autonomous agent with dynamic tool selection and durable conversational memory: on Make you often assemble that logic yourself with HTTP calls and manual state handling.
n8n gives you an agent loop with memory out of the box. Make gives you a fast, shareable agent shell and asks you to build the loop’s harder parts when you need them.
Pricing: Executions vs Credits
The pricing pages look comparable until you remember how an agent actually runs.
n8n bills per execution — one complete workflow run, trigger to final node, counts as a single execution no matter how many nodes or LLM calls happen inside it. Make bills per operation, now called credits, where every module action consumes at least one credit, and AI-related steps can consume more.
| n8n | Make | |
|---|---|---|
| Billing unit | Per execution (whole run = 1) | Per operation / credit (each step counts) |
| Free option | Self-hosted Community (unlimited) | Free cloud: 1,000 credits/mo, 2 scenarios |
| Entry paid plan | Starter — $24/mo, 2,500 executions | Core — $9/mo, 10,000 credits |
| Next tier | Pro — $60/mo, 10,000 executions | Pro — $16/mo; Teams — $29/mo |
| Self-host | Yes, free, no execution limits | No — cloud only |
The gap shows up the moment an agent loops. One request that makes ten tool calls is 1 execution on n8n but roughly ten credits on Make — more once AI steps are counted. At a few hundred runs a day, n8n’s per-execution model pulls dramatically ahead.
The flip side is real too: for a simple, low-volume automation, Make’s $9 Core plan beats n8n’s $24 Starter, and the free self-hosted route only helps if you’ll run your own server.
Make switched from “operations” to “credits” in late 2025, and AI features can consume credits differently from a standard module run. Model a realistic agent workload before you commit to a tier — the headline price is not the price of a chatty agent.
A Real Example: A Support-Triage Agent
To make this concrete, take one task I rebuilt both ways: an agent that reads an incoming support email, decides whether to answer, escalate, or refund, and remembers the customer across messages.
In n8n, the AI Agent node carried most of the weight. I gave it the model, four tools (lookup order, draft reply, escalate, issue refund), and Postgres-backed memory keyed by the customer’s email. The decide-call-read-decide loop was handled for me, and the whole thing was one workflow, billed as one execution per email — even when the agent made five or six tool calls to close a case.
In Make, the first 80% was faster: I uploaded the support policy as context and had a working agent shell quickly. The last 20% — dynamic tool selection and memory across a multi-email thread — is where I added HTTP modules and a small store to track state by hand. It worked. It also put every tool call on the meter.
Then I let it run for a month — about 6,000 emails, roughly 200 a day, each looping through six to eight tool calls. On n8n that’s 6,000 executions, because the whole run counts once, which sits on the $60 Pro plan (10,000 executions). On Make those same tool calls bill individually: at ~7 operations an email, that’s about 42,000 credits a month — more than four times what a Core or Pro plan includes, so you’re buying credit packs on top. Same agent, same work, a very different bill.
Neither was “wrong.” n8n traded a slower start for a cleaner loop and far cheaper runs at volume. Make traded run-cost and loop control for a faster first hour. For an agent that loops and remembers, I’d reach for n8n; for a fast, mostly-linear helper, Make earns its keep.
When to Pick n8n
Choose n8n when the agent itself is the hard part. It’s the better tool if you need a real autonomous loop, you care about data control, or you expect volume.
- You need a true agent — dynamic tool selection, multi-step reasoning, and memory across turns, without hand-rolling the loop.
- Self-hosting or privacy matters — the free Community edition runs on your own infrastructure, and you can point the agent at a local model so no data leaves your network.
- Volume is coming — per-execution billing keeps a chatty, tool-heavy agent affordable as runs scale.
The trade-off is a steeper start — you’re closer to the machinery.
When to Pick Make
Choose Make when speed and sharing beat loop control. For a lot of real automation, that’s the right trade.
- You want it live today — uploading files for context and shipping a no-code agent is genuinely quicker for standard tasks.
- The team is non-technical — Make’s visual model and shareable agents fit people who won’t touch a self-hosted server.
- The task is mostly fixed — lead routing, CRM updates, templated replies — work that doesn’t need the model improvising ten steps deep.
If your “agent” is really a smart, mostly-linear workflow, Make will feel less like over-engineering.
When NOT to Reach for Either
Here’s the part the hype skips: a lot of “AI agent” work doesn’t need an agent at all. If you can write the steps down in advance, a plain automation — or plain code — is cheaper and easier to trust than a model deciding at runtime.
Keep it a fixed workflow when the path never changes, when every run must produce the same result, or when latency and cost are tight. Both n8n and Make run deterministic workflows with zero LLM calls, and for most of a real system that’s still the right answer. Reach for the agent loop only at the one decision that can’t be scripted. (If that line is fuzzy, AI Agent vs Workflow draws it in detail.)
Quick Recap
- The deciding question is who runs the loop — n8n runs it for you; Make hands you the parts.
- n8n — native AI Agent node, built-in memory, 70+ AI nodes, self-hostable, billed per execution. Best for real, tool-using agents and for volume.
- Make — no-code “digital colleague” agents, file-upload context, fast to ship and share, cloud-only, billed per credit. Best for standard tasks and non-technical teams.
- Pricing flips on workload — Make is cheaper for simple, low-volume jobs; n8n is far cheaper for agents that loop through many steps.
- Default to a plain workflow and add the agent loop only where the path truly can’t be fixed.
Conclusion
The n8n vs Make for AI agents decision isn’t about which tool is smarter — it’s about who you want controlling the agent’s reasoning loop and how you want to pay for every step inside it. n8n gives you the loop, the memory, and the option to self-host, at the price of a steeper start. Make gives you a shareable agent fast, at the price of run-cost and loop control once things get genuinely autonomous. Map those to your task and the answer usually picks itself.
What are you building first — a real autonomous agent, or a smart workflow you’re calling an agent? Tell me in the comments, and I’ll point you to the cheaper setup for it.
Read next: How to Build an AI Agent in n8n (No-Code) — a step-by-step build using the AI Agent node, so you can try the n8n side yourself. Or zoom out with the best AI agent frameworks in 2026 if you’re weighing code-first options too.