AI agents are everywhere right now. Twitter feeds, pitch decks, even the coffee chat at co-working spaces — everyone has a story about how they’ve automated half their business overnight. Some of it’s hype, but some of it’s real. I’ve seen tiny teams suddenly act like they’ve got a 10-person ops department running in the background.

But here’s the uncomfortable truth: building an agent is the easy part. Keeping it alive is where most people stumble. Last month, a founder showed me her setup — the agent was supposed to be qualifying leads in real time, but instead it had burned through 50,000 Make.com operations overnight just… looping. (We laughed about it, but her credit card bill wasn’t funny.)

And that’s why the same debate keeps popping up: do you bet on Make.com or do you go with n8n

Yes, there are other platforms. Zapier still feels like the old reliable Toyota Corolla of automation. Microsoft Power Automate is what you get if your IT department forces you to drive a company car. But if you’re building agents in 2025, the real showdown is Make vs n8n. They’re the only ones that balance flexibility with the ability to scale without lighting your budget on fire.

Here’s how I see it: Make.com is glossy, simple, and great when you just want something working today. n8n is rougher around the edges but gives you total control if you’re willing to get your hands dirty. I might be wrong, but I think that split — speed vs control — explains 90% of the founder conversations I’ve had in the past year.

So in this guide, I’ll walk you through the trade-offs. The costs, the limits, the hidden “gotchas.” And maybe even convince you that the boring decision of which automation tool to use is the most important one you’ll make for your AI agents.

Make.com in a Nutshell

Make.com is often the first automation tool founders fall for. It’s glossy, approachable, and honestly pretty fun to use. The interface feels like a creative canvas — you drag bubbles around, connect them, and within an afternoon you’ve got something running that looks and feels impressive.

I’ve seen non-technical teams spin up working prototypes in a weekend: a lead-qualifier here, a Slack-to-Notion sync there, even AI agent workflows stitched together with zero code. On Monday morning, they’re showing it off like they’ve hired an ops engineer.

But that magic comes with a meter running. Every step, every “operation” in Make-speak, adds up. The same prototype that feels effortless at first can quietly burn through tens of thousands of operations in a month. It works beautifully, but the bill hits just as hard.

That said, Make is unmatched for speed. If you care about getting something live today, with minimum friction, it’s the easiest on-ramp into serious automation.

n8n in a Nutshell

n8n takes a different path. It’s open-source at its core, which means you aren’t locked into someone else’s cloud or pricing model. You can run it in n8n Cloud if you want the convenience, or self-host it and run unlimited workflows for the cost of a modest server. That flexibility alone changes the game for teams scaling fast or dealing with sensitive data.

And don’t let old impressions fool you: n8n’s interface today is slick. Workflows animate as they run, so you can literally watch your data flow step by step. Side by side with Make, the experience is closer than you’d expect — with the added bonus that you can drop in API calls or bits of code when things get complicated.

I’ve worked with founders who switched to n8n not because they loved tinkering with servers, but because Make’s per-operation billing made their costs unpredictable. On n8n self-hosted, the same workflow that cost hundreds to run on Make suddenly ran for “free.” For data-heavy or compliance-focused businesses, that predictability and control is hard to beat.

In short: Make gets you moving fast. n8n gives you control. The right pick usually depends on whether you value speed or scalability more in the moment.

Feature Comparison

Hosting & Deployment

Make.com is fully cloud-based. Everything you build runs on their infrastructure, which means you don’t have to think about servers or maintenance. That’s a blessing if you want simplicity, but it also means you’re tied to their environment with no real alternative.

n8n, on the other hand, gives you a choice. You can use n8n Cloud and get a managed experience similar to Make, or you can self-host and keep everything on your own infrastructure. For some teams, especially those in regulated industries, that choice is the difference between being able to use automation at all or not.

Ease of Use

Make is the tool that gets you from zero to something working in record time. The drag-and-drop interface, polished templates, and hand-holding onboarding make it beginner-friendly. I’ve seen founders with zero technical background build useful workflows in a weekend.

n8n has a steeper learning curve, but it’s not the intimidating “developer-only” tool it once was. The visual editor has improved a lot — animated workflows make it easier to see what’s happening in real time — but it still expects you to understand a bit more about data and logic. The upside is you can go much deeper: when Make hits a wall, n8n lets you drop in API calls or even raw code.

Integrations & Flexibility

Make wins on sheer numbers of ready-made integrations. With over 2,000 supported apps, chances are the tool you use is already in their library. The setup process is polished and straightforward, which is a huge time saver if you don’t want to mess around with APIs.

n8n has fewer prebuilt nodes, but it makes up for that with flexibility. If there isn’t a node for your app, you can hit the API directly or wire up something custom. It’s more work, but it means you’re never truly stuck waiting for official support. For technical founders, that freedom matters more than the raw number of integrations.

Pricing & Scaling

This is where the two platforms really diverge. Make charges per operation — every single step in a workflow has a cost. At small scale, that’s fine, but as your automations grow, the bills grow faster than most people expect.

n8n Cloud charges per workflow execution, which can be more predictable. And if you self-host, the only cost is the server you run it on. For teams running heavy AI or data-intensive workflows, that difference can mean hundreds (or thousands) saved each month.

AI & Agent Support

Both platforms are leaning hard into AI. Make has features like the “Make Grid” for visualizing complex automations and support for modular agent architectures through its MCP server. It’s clearly designed to make AI feel approachable to non-technical teams.

n8n takes a more developer-first approach. It’s already used for advanced LLM integrations, retrieval-augmented generation pipelines, and agent-style workflows that need custom logic. If you want fine-grained control over how your AI agent thinks and acts, n8n is the stronger fit.

Data Security & Compliance

With Make, all your data passes through their cloud. That’s fine for most startups, but if you’re in healthcare, finance, or Europe with strict GDPR concerns, it might be a deal-breaker.

n8n’s self-hosting option gives you total control over where your data lives and how it’s handled. For some teams, that isn’t just a nice-to-have — it’s the reason they can use automation at all.

AI Agent Capabilities (2025)

1) Speed to a working agent

If you want something live today, Make.com is fast. Agents are now a first-class feature: you can drop a “Run an agent” module inside a workflow, or spin up an agent that uses your scenarios as callable tools. In practice, that means you can start simple—embed an agent in a Slack-triggered flow—and later let that same agent orchestrate other scenarios for more autonomy. There’s even a step-by-step tutorial flow (context, prompt, run) that feels more like onboarding a teammate than wiring a bot.

n8n isn’t lagging here. Their AI Agent node lets you do the same in a visual way: create an agent, give it a model and memory, then hand it tools. It’s still more configurable than Make (and asks more from you), but you’re not starting from scratch—it’s drag, drop, and iterate.

2) Tool use & autonomy

Agents only matter if they can actually do things.

• On Make, the key innovation is the MCP Server (Model Context Protocol). That’s how you expose your scenarios as safe, typed tools an agent can call on its own. Combined with Scenario Outputs (structured results you can pass back), this turns an LLM from “chatty assistant” into an actual process runner.
• On n8n, the setup is: “give the agent tools, then let it reason.” Tools can be APIs, web searches, DB queries, or even vector stores (added in early 2025). Their team has been pushing “agentic RAG” workflows, where an agent plans, retrieves, and executes without extra glue code.

My take? Make’s MCP lowers the barrier for non-dev teams to give agents real powers. n8n’s toolchain is broader if you’re comfortable assembling the pieces yourself.

3) Memory & context

• Make handles memory through Agent Context modules: you can create, list, or delete context (text or files) and pass it into a run. It’s tidy and auditable.
• n8n lets you add persistent memory inside the agent and now treat vector stores as native tools. Their official examples (like a Gemini agent with memory) show durable recall in action.

Both approaches work, but memory is where things break in practice—if you’ve ever debugged an agent looping because it “forgot” five minutes ago, you know why observability matters.

4) Observability & control

• Make recently launched Make Grid: an auto-generated map of your entire automation landscape—agents, scenarios, data paths. For ops people, it’s like finally having X-ray vision across everything your AI is touching.
• n8n takes a more granular approach: animated runs show data flowing node by node, with detailed logs. It’s not cross-org mapping like Grid, but when you’re live-debugging an agent with a client on Zoom, the step-by-step view saves the day.

5) Model ecosystem

Both platforms avoid lock-in:

• Make integrates directly with Claude, OpenAI (and Azure OpenAI), plus others. Plug-and-play modules mean you can swap providers with minimal fuss.
• n8n ships nodes for OpenAI, Anthropic, and Google Gemini, so you can pivot between models without re-architecting.

6) Where each wins for agents

If you need a polished agent this week—customer support assistant, lead-qualifier, content generator—Make.com is the faster path. The modules are opinionated in the right ways, MCP unlocks tool use without code, and Grid gives you oversight.

If your agent needs RAG, custom tools, or lives in a compliance-heavy environment, n8n is stronger. Vector-store tools, custom logic, and the option to self-host mean you won’t hit a ceiling—or a surprise bill—halfway through scaling.

When to Choose Which?

Here’s the part most founders care about: which platform should you actually bet on for your AI agents?

If you’re early-stage, moving fast, and don’t have technical depth on your team, Make.com is your friend. You’ll get something working today, without worrying about servers or APIs. Need a customer support agent that escalates to Slack, or a simple lead-qualifier that syncs with HubSpot? Make will get you there in a weekend. The trade-off is predictability—you’ll pay for every single operation, and those bills creep up faster than you think.

If you’re technical (or at least not afraid to tinker), and especially if your agent is going to chew through a lot of data, n8n is the safer long-term bet. Self-hosting means your marginal cost is basically zero. That’s a lifesaver once your AI workflows scale beyond “cute demo” into “production system.” Plus, if you’re in a regulated space or just paranoid about data leaving your control, n8n lets you sleep at night.

So the rule of thumb I give founders is this:

• Choose Make.com when you value speed over control. You want something live today, and you’ll figure out costs later.
• Choose n8n when you value control over speed. You’re building heavy AI workflows, expect scale, or need compliance guarantees.

And if you’re still on the fence? Start with Make to get traction, then migrate to n8n when the costs or compliance headaches catch up with you.

Conclusion

AI agents don’t sleep. Why should your business?

That’s the real promise behind both Make.com and n8n: giving you a backbone that keeps your automations and agents running when you’re offline. Make gets you moving fast, with less friction and friendlier onboarding. n8n gives you control, cost predictability, and the option to keep everything in your own hands.

Neither is “the right” answer for everyone. The right choice depends on your stage, your workload, and your appetite for trade-offs. But what matters most is this: don’t stall your business because you’re paralyzed by the tool debate. Pick the platform that gets you moving, then evolve when the stakes demand it.

And if you’d rather not wrestle with integrations, hosting, or debugging loops at 2 a.m.—that’s where I come in. My work is simple: replicate yourself, automate everything, and make sure your agents actually ship.

Got an AI project stuck between prototype and production? Let’s fix it.