In short: Make is the right choice for non-technical teams that want simple, ready-to-use cloud workflows, while n8n wins wherever data control, high operation volumes, complex logic and AI integration matter. Both are excellent automation platforms — the difference comes from your business context, not from an absolute ranking.
At WebForge we have been building n8n automations and Make scenarios for clients in Romania for several years, so we have seen both platforms at work in real conditions: from simple form syncs to AI workflows with dozens of steps. In this article we compare the two platforms honestly, criterion by criterion, so you can decide with full knowledge.
n8n vs Make: what is the fundamental difference?
The essential difference is the product model: Make is a pure SaaS, hosted exclusively in their cloud, while n8n is source-available (fair-code licensed) and can run on your own servers or in their cloud edition. Almost everything else flows from this difference in philosophy: pricing, data control, flexibility and target audience.
Make (formerly Integromat) was built around the idea of accessibility: anyone on the marketing or operations team can open the visual editor and connect apps without writing code. n8n was built around the idea of control: developers and technical teams get a tool they can extend with code, host wherever they want and integrate deeply into their own infrastructure.
Neither approach is "better" in absolute terms. The right question is not "which platform is superior" but "which one fits your team, your data and your volume".
Pricing model: credits vs self-hosting
Make charges on a credit basis (model introduced in August 2025): every executed module consumes credits, with paid plans starting at around 9–10 EUR per month, while n8n is free if you host it yourself — you only pay for the server — or has a cloud edition billed mainly per workflow execution, not per individual step. This difference in model becomes decisive at high volumes.
Let us break it down, with the important note that third-party pricing changes frequently — the figures below are indicative for mid-2026 and we always recommend checking the official pricing pages before making a decision:
- Make: has a limited free plan, useful for testing, and paid plans start at around 9–10 EUR/month for a bundle of credits (the credit system replaced the old per-operation model in August 2025). Beware: every module executed in a scenario consumes credits. A 10-step flow running 1,000 times a month consumes around 10,000 credits — check the official page for each plan's exact allowance. With long, frequent flows, the bill grows quickly.
- n8n self-hosted: the software itself is free for your own use (fair-code license, with certain commercial restrictions for those who would sell n8n as a service). The real cost is hosting — a modest VPS, in the range of a few euros per month, easily handles thousands of executions — plus maintenance: updates, backups, monitoring.
- n8n Cloud: the edition hosted by n8n is billed mainly per workflow execution, not per step. That means a 50-node flow that runs once counts, broadly speaking, as a single execution. Starter plans are, indicatively, in the 20–25 EUR/month range — again, check the official page for up-to-date figures.
The practical conclusion: at low volumes the cost difference is negligible; at high volumes or multi-step flows, Make's credit-based model becomes visibly more expensive than self-hosted n8n or even n8n Cloud. We have migrated clients from Make to n8n for exactly this reason, after the monthly bill exceeded the cost of a dedicated server several times over.
Learning curve: how fast do you become productive?
Make is easier to learn for non-technical people, while n8n requires a bit more "programmer thinking" but rewards you with far more flexibility. The difference is not dramatic, but it is real and worth weighing depending on who will build and maintain the automations.
Make's visual editor is, in our opinion, the friendliest in the industry: round modules connected by curved lines, drag-and-drop data mapping, and each step's results immediately visible. A marketer with no technical background can build their first working scenario on day one.
n8n uses a node-based canvas that will feel familiar to anyone who has worked with developer tools. The core concepts — nodes, connections, expressions — are quick to learn, but n8n exposes the data structure more directly (JSON, arrays, items) and expects you to understand how data flows between nodes. For a developer, that is a clear advantage: nothing is "hidden under the hood". For someone completely non-technical, the first weeks can be steeper.
Self-hosting and data control: a clear win for n8n
If you work with sensitive data — personal, medical, financial — or have strict GDPR and data sovereignty requirements, n8n wins hands down, because it is the only one of the two that can run entirely on your infrastructure. Make processes everything in its own cloud and does not offer a self-hosted option.
What this means concretely for a company in Romania or the EU:
- Data never leaves your infrastructure. With self-hosted n8n, customer data passes only through your servers (or your European hosting provider's), which significantly simplifies GDPR compliance analysis.
- Credentials stay with you. API keys, tokens and connection passwords live in your database, encrypted with your key — not in a third-party SaaS.
- You do not depend on a vendor's availability. You can run n8n on an internal network, behind a VPN, even without internet access for internal flows.
- Full audit. Since the code is open, teams with strict security requirements can inspect exactly what the platform does.
Honestly speaking, self-hosting also comes with responsibility: you are accountable for updates, backups and securing the server. For clients who do not want that burden, we set up managed n8n instances — a middle-ground model that preserves data control without the operational headaches. If that interests you, write to us and we will discuss your specific scenario.
AI capabilities and custom code: where n8n pulls ahead
For AI automations and custom logic, n8n offers more power: native JavaScript and Python code nodes, a dedicated layer of AI/LLM nodes and the freedom to integrate any model or library. Make also has AI modules and a code function, but its flexibility ceiling is lower.
In n8n you can, among other things:
- write JavaScript or Python code directly in a node, for data transformations, validations or logic that no predefined module covers;
- build AI agents with dedicated nodes for LLMs (OpenAI, Anthropic, locally run open-source models), conversational memory, tools and vector databases for RAG;
- combine self-hosting with local models — a precious detail when the data sent to AI is not allowed to leave the company;
- call any API through fully configurable HTTP nodes, with pagination, retries and exotic authentication schemes.
Make has also evolved nicely on the AI front — it has modules for the major LLM providers and visually configurable agents — and for standard cases (summarisation, classification, generated replies) it is perfectly capable. The difference shows up in non-standard cases: custom RAG pipelines, multi-agent orchestration, complex data processing before and after the AI call. There, the ability to "drop down into code" makes n8n the more powerful tool. We covered these architectures in our complete AI automation guide.
Integrations: who connects to more apps?
Make offers more prebuilt integrations — around two thousand apps — compared to n8n, which has native nodes in the hundreds plus an active community of third-party nodes. On paper, Make leads; in practice, the difference matters less than it seems.
Why? Because both platforms have generic HTTP modules through which you can connect to any service with an API, even if no off-the-shelf integration exists. In n8n, the HTTP Request node plus the ability to write code covers practically anything; the community additionally publishes hundreds of installable nodes. In Make, the HTTP modules do the same job, but with somewhat less flexibility for unusual authentication scenarios.
Our practical recommendation: before choosing, list the 5–10 apps critical to your business and check on the official sites whether each platform has a native integration. A well-maintained native integration saves hours of configuration compared to a hand-built one.
n8n vs Make comparison table
| Criterion | Make | n8n |
|---|---|---|
| Pricing model | Credit-based (each executed module consumes credits); plans from ~9–10 EUR/month | Self-hosted free (you pay hosting) or cloud per workflow execution |
| Cost at high volume | Grows proportionally with operations; can get expensive | Nearly flat when self-hosted; clear advantage |
| Learning curve | Very accessible to non-technical users, excellent visual editor | More technical; rewards data/code knowledge |
| Self-hosting | Not available | Yes — full control over data and infrastructure |
| GDPR / sensitive data | You depend on Make's cloud | Data can stay entirely on your servers |
| Custom code | Limited (functions, restricted code modules) | Native JavaScript and Python nodes, no practical ceiling |
| AI / LLM | Solid AI modules for standard cases | Dedicated AI nodes, agents, RAG, local models — more flexible |
| Prebuilt integrations | More (in the thousands) | Fewer native (in the hundreds) + community nodes + HTTP |
| Maintenance | Zero — everything is managed by Make | When self-hosting, you (or your partner) manage the server |
| Ideal audience | Non-technical teams, SMBs with simple flows | Technical teams, sensitive data, high volumes, complex AI |
When to choose Make
Choose Make if your team has no technical people, your flows are relatively simple and you want a SaaS solution that works from day one, with zero infrastructure worries. This is the scenario where Make's strengths truly shine.
Concretely, Make is our recommended choice when:
- automations will be built and maintained by marketing, sales or operations, not by IT;
- flows are short and moderate in volume — notifications, CRM syncs, forms to email, scheduled posts;
- your critical apps have good native integrations in Make;
- you do not handle data with special confidentiality requirements;
- you want small, predictable costs at the start and zero maintenance.
When to choose n8n
Choose n8n if you work with sensitive data, have high volumes where credit-based billing becomes costly, need complex logic or are building serious AI automations. It is the platform we recommend for "infrastructure-grade" automations that become part of your company's backbone.
Concretely, n8n is the right choice when:
- GDPR or internal policies require data to stay on your servers or in the EU, under your direct control;
- you run thousands or tens of thousands of executions monthly and want near-fixed costs;
- flows have complex branching, non-trivial data transformations or steps that require JavaScript/Python code;
- you are building AI agents, RAG pipelines or integrating LLMs (including local models);
- you have someone technical on the team (or as a partner) to manage the instance.
Our verdict: it depends on the case, and that is an honest answer
There is no universal winner in the n8n vs Make duel: Make is the best gateway into automation for non-technical teams, and n8n is the best long-term foundation for companies with technical requirements, high volumes or sensitive data. We implement both platforms and recommend them to clients on exactly this fit criterion, not on personal preference.
A pattern we often see in practice: companies start with Make for speed, and as volumes and complexity grow, they migrate critical flows to self-hosted n8n — for cost and control. Both stages are correct decisions at their moment. The important thing is not to stay stuck on an ill-fitting platform just because migration seems complicated; with good planning, flows can be ported systematically.
If you want a recommendation applied to your situation — the apps you use, the estimated volume, the data requirements — contact us. We do a short, no-commitment analysis and tell you honestly which platform makes sense for you, even if the answer is "the simplest option, without us".
Frequently asked questions
Can I migrate from Make to n8n (or the other way) without starting from scratch?
Flows do not export automatically from one platform to the other, but their logic can be rebuilt systematically: same trigger, same steps, same data mappings. For well-documented flows, migration is a matter of hours, not weeks. We have done migrations in both directions.
Is n8n really free?
The self-hosted software is free for internal use, under a fair-code license (which restricts reselling n8n as a service, not using it inside your company). The real costs are the server — typically a few euros per month — and administration time. The n8n Cloud edition is paid; check the official pricing page for up-to-date figures.
Which platform is better for AI automations?
For standard cases (summarisation, classification, generated replies), both work well. For AI agents, RAG, local models and custom logic around LLMs, n8n offers significantly more flexibility thanks to its dedicated AI nodes and custom code. Details in our AI automation guide.
What if I have no technical people on the team but want n8n?
You have two options: n8n Cloud, which removes the server burden, or a self-hosted instance managed by a technical partner. We offer installation, managed hosting and maintenance for n8n instances, plus building the flows — write to us and we will propose the option that fits your budget.