Make vs n8n: An Honest Comparison So You Don't Overpay

If you're comparing automation platforms and price matters to you (spoiler: at scale, it's going to matter a lot), this is the comparison you need. n8n and Make solve the same problem—connecting apps and automating processes—but with opposite models: Make charges for every operation you run on its cloud, while n8n is open source and you can self-host it at a fixed cost, with no execution limit. For small flows, Make is faster to get started; once volume grows, n8n pays for itself. Let's get to the numbers.

n8n vs Make: the difference that defines everything is how they charge

Here's the heart of the matter, and where most comparisons stumble:

• Make charges per operation: every module that runs inside a scenario consumes an operation. If your scenario has 8 modules and processes 100 records, you've just spent 800 operations. This makes cost grow with the complexity of the flow, not just with volume.
• n8n counts executions: an execution is the entire workflow, whether it has 3 nodes or 40, processes 1 item or 5,000. And if you self-host it, even that doesn't matter: unlimited executions for the price of the server.

That conceptual difference explains why so many LATAM companies that started with Make end up with bills that triple their budget: they built richer scenarios, and every improvement raised their unit cost.

2026 pricing table: what you pay in each scenario

Monthly volume	Make	n8n Cloud	n8n self-hosted
1,000 operations / ~200 executions	Free	USD 24/month	USD 10-15/month (VPS)
10,000 operations / ~2,000 executions	USD 10.59-18.82/month	USD 24/month	USD 10-15/month
80,000 operations / ~15,000 executions	USD 75-130/month	USD 60/month (Pro)	USD 15-25/month
500,000+ operations	USD 400-700/month	Negotiable	USD 25-50/month

Two honest caveats:

1. The operations↔executions equivalence is approximate (we assume scenarios of 5-8 modules). If your flows are simple, Make performs better than the table shows.
2. Self-hosting has a hidden cost: someone has to install, update, and monitor the instance. We cover it in detail in how much it costs to host n8n.

What happens when volume grows?

This is the question you need to answer before choosing, not after.

With Make: growth is linear and uncapped. Going from 10,000 to 100,000 operations multiplies your bill almost tenfold. And there's no escape: there's no self-hosted version of Make, so your only lever is to simplify scenarios (fewer modules = fewer operations), which runs exactly counter to automating better.

With n8n self-hosted: growth is tiered. Your USD 15 VPS handles tens of thousands of monthly executions; when it gets tight, you move up to one at USD 30-50. A company processing 500,000 events a month pays the same as one processing 50,000, plus a bit more RAM.

The rule of thumb we use at Deepyze: if your Make bill exceeds USD 50-100/month for three months straight, you're already funding your own n8n instance with money you throw away every month.

Which is easier if you're not technical?

Direct answer: Make is easier for a non-technical user, and that's not up for debate. Its circular editor is more visual, its modules come pre-configured with fewer cryptic fields, and data mapping is done with clicks instead of expressions.

n8n has improved its usability a lot, but it still assumes you understand what JSON is, what an expression like {{ $json.email }} is, and how an API response is structured. For an administrative profile with no technical support, that's a real barrier.

Now, the catch: that ease of Make evaporates when the flow gets complicated. A scenario with 4 conditional branches, error handling, and retries turns into a tangle of routers that's hard to maintain. In n8n, that same flow is solved with one Switch node, a 10-line Code node, and native error workflows. Make is easier to learn; n8n is easier to maintain when things get serious.

Not sure whether your volume justifies migrating? Book a 30-minute call and we'll calculate the real cost of each option with your numbers, no strings attached.

Complex scenarios: where n8n pulls ahead

• Embedded code: n8n lets you run JavaScript or Python inside the workflow. Make has function modules, but far more limited ones.
• Workflows that call workflows: in n8n you build reusable sub-workflows (one that normalizes phone numbers, another that logs errors) and invoke them from any flow. Make's modularity is more rudimentary.
• AI integrations: n8n has native nodes for agents, memory, and tools — the foundation of any serious AI integration project. Make integrates with OpenAI, but orchestrating an agent with tools is forcing the tool.
• Your own APIs: both have HTTP modules, but n8n handles pagination, odd authentication, and nested responses better. If your stack includes internal systems, you'll probably also need API development to expose that data cleanly.

Real case: migrating from Make to n8n, with numbers

A consumer-goods distributor from the interior of Argentina came to us with this picture:

• 23 scenarios in Make: order syncing, stock alerts, invoicing, reports, and notifications to sales reps.
• Make bill: USD 214/month (Teams plan, ~290,000 monthly operations) and rising ~8% per month as they added clients.
• Extra pain: two critical scenarios shut off when they hit the operation limit at month-end, right at the invoicing close.

The migration:

• Duration: 5 weeks (3 rebuilding workflows, 2 running in parallel to validate).
• Project cost: equivalent to about 4 months of their Make bill.
• New infrastructure: USD 28/month VPS with Docker, backups, and monitoring.

Result at 12 months: net savings of ~USD 1,700 in the first year (after deducting the migration cost) and ~USD 2,500/year from then on, with unlimited volume. The least expected benefit: by no longer paying per operation, they started automating processes that previously "weren't worth the expense." That shift in mindset—automating without watching the meter—is usually worth more than the direct savings, and it's the core of our AI automation service.

When n8n is NOT for you (and Make is)

To make this comparison genuinely honest:

• Low volume and simple flows: if you run fewer than 10,000 operations a month with 3-4 module scenarios, Make Free or Core solves your life without thinking about servers.
• Zero internal technical capacity and no partner: a self-hosted instance without maintenance is a time bomb (security updates, backups, full disk). If nobody is going to care for it, Make cloud is safer.
• You need results tomorrow: building a scenario in Make takes an afternoon. Installing and securing self-hosted n8n does not.
• Your team already masters Make and the bill is reasonable: migrating out of ideology has no ROI.

If what you're comparing against is Zapier instead of Make, the logic is similar but the numbers change quite a bit: we break it down in n8n vs Zapier.

Verdict: which to choose based on your situation

• Trying automation for the first time, with no technical person: Make.
• More than 50,000 operations/month or a bill above USD 50: n8n self-hosted.
• Sensitive data (health, finance, customers' personal data): n8n self-hosted, no question — your data never leaves your server.
• Flows with AI, agents, or your own APIs: n8n, for technical capability.

At Deepyze we implement and maintain n8n instances for companies across LATAM: we map your processes, migrate whatever you have in Make or Zapier, and leave the instance monitored and backed up. All at a fixed price, with a team in your time zone and no surprises on the bill. Tell us about your case and within 24 hours you'll have a concrete proposal with numbers.