n8n AI vs Make AI: Open Source vs No Code AI Automation
This comparison keeps coming up, and for good reason.
n8n and Make are the two tools most teams land on after they realize Zapier is eating their budget. Both can handle serious automation workloads. Both have real AI capabilities. Both will outlast the hype cycle.
But they make a fundamentally different bet on who’s building the workflows.
Make bets you want speed and accessibility. n8n bets you want control and unlimited execution. Pick based on which of those actually describes your team.
Quick verdict: Make for non-technical teams and fast deployment. n8n for technical teams, data-sensitive environments, high-volume workloads, and anyone who wants to run serious AI agent workflows without paying per operation.
What Make AI actually is
Make (formerly Integromat) is a cloud-based visual automation platform. You build workflows – called scenarios – by dragging and dropping modules onto a canvas and connecting them with lines. No code required. Everything runs in Make’s cloud.
In 2026, Make has native AI modules for OpenAI, Anthropic Claude, and Google Gemini. AI Agents built directly on the canvas can choose optimal paths dynamically instead of following hardcoded logic. Make Grid gives teams a visual map of their entire automation landscape across scenarios.
The credit system shifted in late 2025. Make moved from operations to credits as the billing unit, with variable consumption – standard actions cost one credit, while AI modules and data-intensive operations can consume multiple credits per run.
3,000+ app integrations. Fully cloud-hosted. 1,000 free operations per month. Paid plans from $9/month.
What n8n AI actually is
n8n is an open-source workflow automation platform. You can self-host it on your own server for essentially free, or use their managed cloud. The key architectural difference: an execution in n8n is one complete run of an entire workflow, regardless of how many steps it contains.
A 20-step workflow that runs 1,000 times costs 1,000 executions. The same workflow in Make costs 20,000 operations. That gap is why technical teams choose n8n.
The AI story is genuinely strong. n8n has 70+ dedicated AI nodes including OpenAI, Anthropic, Google Gemini, Hugging Face, and local LLMs via Ollama. It has native LangChain integration for building RAG (Retrieval Augmented Generation) systems that pull from your own data.
You can write custom JavaScript or Python at any step. And an AI Workflow Builder converts natural language descriptions into complete functional workflows.
1,200+ integrations. Self-hostable. Cloud plans from roughly $24/month (€24). Self-hosted Community edition is free.
I covered how Make compares to Zapier in more depth in the Zapier AI vs Make AI comparison – worth reading alongside this one if you’re evaluating all three.
The fundamental difference
Make pushes complexity into the interface. n8n pushes complexity into your brain.
In Make, building a complex workflow means more clicks, more configuration, more visual connections. The platform handles a lot for you. You’re moving pieces around.
In n8n, building a complex workflow means more code, more control, more decisions you make yourself. The platform gives you the tools. You write the logic.
Neither is objectively better. They’re optimized for different people. Make gives non-technical users a path to serious automation. n8n gives technical users unlimited depth without artificial pricing ceilings.
The question that settles this: does your team have someone who can write a bit of JavaScript and SSH into a server? If yes, n8n. If no, Make.
Pricing: The real cost comparison
This is the section that matters most.
| Make | n8n Cloud | n8n Self-Hosted | |
|---|---|---|---|
| Free tier | 1,000 credits/month | 14-day trial only | Free (Community Edition) |
| Entry paid | $9/mo (10,000 credits) | ~$24/mo (2,500 executions) | $10-20/mo server cost |
| Mid tier | $16/mo (10,000 credits + priority) | ~$60/mo (10,000 executions) | Same – unlimited executions |
| Team plan | $29/user/mo (10,000 credits shared) | ~$800/mo (Business, 40,000 exec) | Business license + hosting |
| Billing unit | Per credit (1 per module, more for AI) | Per execution (entire workflow = 1) | N/A – unlimited |
| 10,000 complex-workflow executions | Thousands of credits – $50-100+/mo | $60/mo (Pro) | ~$10-20/mo |
| Can self-host | No – cloud only | Technically yes, but defeats point | Yes – primary use case |
| Data stays on your servers | No | No | Yes |
| Rollover credits | Yes – 1 month rollover | No | N/A |
The pricing difference at volume is stark.
A WhatsApp automation handling 50 daily conversations can consume up to 15,000 Make credits per month. At the same volume in n8n self-hosted: one execution per conversation, 1,500 executions total, unlimited steps, server cost of $10-20/month.
At 20,000 monthly executions, the comparison is roughly $10/month self-hosted n8n versus $60-100+ on managed platforms.
That gap only widens as complexity grows.
Tip: Make’s credit system now charges variable amounts for AI modules. Building an AI-heavy scenario in Make – where Claude or OpenAI processes data on each item in a loop – can burn 5-10 credits per module run. Model your actual credit consumption on a test workflow before committing to a plan. n8n’s AI costs come from your API key charges (passed through directly to OpenAI/Anthropic), not from n8n itself.
AI capabilities: Where the real difference shows
Both platforms connect to major AI providers. But the depth is different.
Make’s AI gives you modular connectors. You can run text generation, classification, summarization, and translation through a pre-built module.
The AI Agent feature on the canvas is genuinely useful. For most business automation use cases – enriching leads, summarizing emails, routing tickets – Make’s AI is sufficient and accessible.
n8n’s AI is built for developers who want to go deep. The 70+ AI nodes include everything from vector databases to embedding generation to LangChain agent orchestration.
You can build a RAG system that ingests your company documentation, embeds it into a vector store, and answers questions from that data – all within a single n8n workflow. You can run local LLMs via Ollama and pay zero API costs.
The difference shows up when your AI workflow needs more than “call GPT and get a response.” State tracking, tool use, multi-step reasoning, connecting LLMs to private data sources – n8n handles all of this natively. Make handles it to a point, then hits walls that require workarounds.
Feature comparison
| Feature | Make | n8n |
|---|---|---|
| Visual workflow builder | Excellent – canvas-based, intuitive | Good – node-based, steeper curve |
| Branching and routing | Yes – routers built-in | Yes – native |
| Loops and iteration | Yes – iterators native | Yes – native |
| Custom code execution | Limited – HTTP module | Yes – JavaScript and Python at any node |
| AI agent builder | Yes – canvas-integrated | Yes – 70+ AI nodes, LangChain |
| RAG / vector database support | Limited | Yes – native, full LangChain stack |
| Local LLM support (Ollama) | No | Yes |
| Self-hosting option | No | Yes – free Community Edition |
| Data sovereignty | No – cloud only | Yes – self-hosted |
| Git version control | No | Yes – Business plan |
| Custom node creation | No | Yes – JavaScript |
| Error handling | Advanced | Advanced |
| Webhook support | All paid plans | All plans including free |
| Operations rollover | Yes – 1 month | N/A |
| App integrations | 3,000+ | 1,200+ cloud; unlimited via HTTP node |
| GDPR / EU data residency | Yes (cloud EU) | Yes (self-host anywhere) |
| Community size | 50,000+ forum members | 45,000+ forum members, open source contributors |
| Learning curve | Low to medium | Medium to high |
| G2 rating | 4.7/5 | 4.8/5 |
Where Make clearly wins
Setup speed. A non-technical team member can build a working multi-step scenario in Make within a few hours. n8n takes 5-10 hours minimum to become productive, and more to build anything complex.
The visual experience for debugging is also genuinely better in Make.
When something breaks, the canvas shows you exactly where, with color-coded data flows. n8n is good but requires more comfort with the interface to debug effectively.
For teams without any developer resources, Make is often the only practical option. Not because n8n is harder – but because self-hosting n8n properly (Docker, PostgreSQL, SSL, queue mode for scale) requires someone who can manage infrastructure. If that person doesn’t exist in your org, it’s not really a free tool.
Make’s 3,000+ native integrations also exceed n8n’s 1,200+ pre-built nodes, though n8n’s HTTP node can connect to any REST API.
The difference matters for non-technical users who want plug-and-play connectors for niche tools without building custom HTTP requests.
And the free plan with 1,000 monthly credits – permanently, not just a trial – gives teams a genuinely useful starting point to test real workflows before spending anything.
Where n8n clearly wins
Execution-based pricing is the headline advantage. One complete workflow run = one execution, regardless of steps or data processed. A 50-step workflow that loops through 500 records in a single run: one execution. The same workflow in Make: 25,000+ credits.
Self-hosting is a real option, not a compromise.
The Community Edition is the full platform with all integrations, all AI nodes, and unlimited executions – for the cost of a $10-20/month server. For teams that have any DevOps capability at all, this changes the economics entirely.
Data sovereignty is the other major factor.
Make is cloud-only. Your automation data, including any sensitive fields passing through your workflows, runs through Make’s infrastructure. For companies in regulated industries (healthcare, fintech, legal) or those with strict GDPR obligations, the only real option is self-hosted n8n.
The AI depth is also genuinely ahead of Make. LangChain integration, vector databases, local LLMs, RAG systems, multi-agent orchestration – n8n’s AI capabilities are at the infrastructure level, not the module-connector level. Teams building AI-native applications (not just AI-augmented workflows) will hit Make’s ceiling faster than n8n’s.
And custom code at any step is n8n-only.
When your workflow needs a one-off data transformation, a regex parsing operation, or a custom business logic function, you write three lines of JavaScript inside a node. In Make, you’re working around the limitation.
The self-hosting reality check
Saying n8n self-hosted is “free” is technically accurate and practically misleading.
Running n8n properly in production requires: a VPS or cloud server ($10-20/month minimum), Docker and PostgreSQL configured correctly, SSL certificates, queue mode for anything handling real load, and someone to apply updates, monitor logs, and debug when workflows break unexpectedly.
That maintenance cost is real. It’s not measured in dollars. It’s measured in developer hours – and developer hours are expensive.
For a solo founder comfortable with Docker basics: n8n self-hosted is genuinely free and excellent.
For a 10-person SaaS company with no technical co-founder: n8n Cloud at $60/month is probably the right call, and Make at $9-29/month might be right too depending on workflow complexity.
“Self-hosted is free” is the most common misunderstanding that leads teams to start with n8n, hit infrastructure issues, and either spend days debugging or give up and switch to a cloud tool.
If you choose self-hosted: expect to invest a serious day getting it set up right the first time. After that, it runs itself.
AI workflow use case matching
Here’s how to match the tool to the actual work.
You need Make if: Your team is non-technical. You’re automating standard business operations – CRM sync, lead routing, Slack notifications, email processing.
You want to deploy working automations this week. You need 3,000+ native integrations. Your data volumes don’t trigger credit ceiling issues.
You need n8n if: Your team has a developer or technically capable ops person.
You’re building AI-native workflows – RAG systems, multi-agent orchestration, custom LLM integrations.
Your workflow executions are high-volume or complex enough that per-operation pricing is painful. You handle regulated data and need on-premise control. You want to use local LLMs without API costs.
Use both if you’re running a technical team that also has non-technical members who need to build automations. n8n for the heavy technical workflows. Make for the quick wins the ops team builds themselves.
This is a real split that works well in practice for growing SaaS teams building out their automation stack.
One more thing on AI agents
n8n is rapidly becoming the platform of choice for serious AI agent workflows in 2026.
The combination of LangChain integration, custom JavaScript, vector databases, local LLM support, and unlimited executions makes it the only no-code-adjacent platform that can handle the full AI agent stack without hitting artificial limits.
Make’s AI agents are genuinely useful for business automation use cases that need some intelligence. But if you’re building systems where agents chain multiple LLM calls, use tools, maintain state, query custom data stores, and loop across complex reasoning paths – you’ll hit Make’s ceiling.
n8n is where those workflows end up.
Bottom line
Make wins on accessibility, deployment speed, and native integration breadth. For teams without technical resources, it’s the right call.
n8n wins on cost at scale, data sovereignty, AI depth, and unlimited execution. For teams with any technical capability, the economics and capabilities are hard to match.
The $9/month Make entry price is genuinely attractive. But if your workflows grow past simple connectors – or if you’re building real AI agent systems – you’ll end up rebuilding in n8n anyway. Might as well make the right call the first time.
If you’re working out which automation stack makes sense for your actual growth motion, reach out. Happy to look at your specific use case and give you an honest opinion.
