Ask around in any automation or no-code community and you’ll get two confident, completely opposite answers about n8n versus Make. One person swears n8n is the only serious choice for anything beyond toy workflows. The next says Make is faster, cleaner, and does everything they need. Both are right — about their own situation.
n8n and Make solve the same basic problem — connecting your apps so work happens automatically — but they’re built for different people. One assumes you’re comfortable with code and want to run your own infrastructure. The other assumes you want a visual, cloud-hosted tool that works the moment you sign up. Picking based on hype instead of fit is how teams end up either fighting a tool that’s too rigid or paying for power they never use.
This guide breaks down the real differences — hosting, pricing, AI features, and ease of use — so you can match the tool to your actual situation instead of whoever posted most recently in your feed.
n8n vs Make: The Short Answer
Choose Make if you want the fastest path to a working automation, don’t have a developer on hand, and prefer a fully managed cloud tool with a large library of ready-made integrations.
Choose n8n if you want to self-host for cost or data-control reasons, need custom code and deep AI agent workflows, or expect high enough volume that per-operation cloud billing would get expensive.
In one line: Make optimizes for speed and simplicity, n8n optimizes for control and cost at scale. Here’s what drives that split.
n8n vs Make at a Glance
| What matters | n8n | Make |
|---|---|---|
| Hosting | Self-hosted (free) or cloud | Cloud only |
| Best for | Technical teams, developers | Non-technical users, fast setup |
| Learning curve | Steeper | Gentler |
| Builder | Node-based, supports custom code | Visual drag-and-drop, more polished |
| Integrations | Fewer native, but any API via HTTP request | Larger pre-built connector library |
| Pricing model | Per execution (cloud) or free (self-hosted) | Per operation |
| Cost at high volume | Generally lower, especially self-hosted | Can climb quickly on complex workflows |
| AI agent support | More configurable, native LLM nodes | Improving, added AI agent features in 2026 |
| Data control | Full control if self-hosted | Data lives on Make’s cloud |
The Real Difference: Who Each Tool Is Built For
This is the split that decides everything else. n8n assumes you’re comfortable with — or willing to learn — a more technical way of working. Its node-based builder supports custom JavaScript and Python, HTTP request nodes for connecting to virtually any API, and the option to self-host on your own server. That’s real power, but it comes with real setup: OAuth configuration, expressions, and a learning curve that non-developers often find steep.
Make assumes you want to get an automation running today without touching code. Its visual, colorful builder guides you through connecting apps with pre-built modules, and its cloud hosting means there’s no server to manage. The trade-off is less flexibility for genuinely custom or unusual logic, and no option to run it anywhere but Make’s own cloud.
Neither approach is wrong. The mismatch happens when a non-technical team adopts n8n expecting a plug-and-play experience, or a technical team outgrows Make’s visual builder and starts fighting its limits.
Hosting and Data Control
This is n8n’s clearest structural advantage: it can be self-hosted. You can run n8n’s free, open-source Community Edition on your own server — a small cloud instance is often enough for moderate workloads — which means your workflow data never leaves infrastructure you control. For teams in regulated industries, or anyone with strict data-governance requirements, that’s not a nice-to-have, it’s often the deciding factor.
Make has no self-hosting option. It’s a fully managed cloud platform, which is precisely why it’s easier to start with — no server to provision, patch, or maintain. But your data lives on Make’s infrastructure, and there’s no way around that if data residency is a hard requirement for your business.
Pricing in Real Numbers (n8n vs Make, 2026)
Both tools changed their pricing in the last year, so here are the current list prices. Prices are in USD and were checked against several up-to-date pricing breakdowns in October 2026. Both vendors adjust plans often, so confirm on their pricing pages before you buy.
n8n pricing
| Plan | Price (monthly / annual) | Usage included | Good for |
|---|---|---|---|
| Community Edition | Free (self-hosted) | Unlimited workflows and executions | Technical teams willing to run a server |
| Starter (cloud) | $24 / $20 per month | 2,500 executions, 5 run at once | Solo users and small teams |
| Pro (cloud) | $60 / $50 per month | 10,000 executions, 20 run at once | Growing teams |
| Business (self-hosted license) | $960 / $800 per month | 40,000 executions, SSO, Git, environments | Companies that need governance |
| Enterprise | Custom | Custom | Large organisations |
There is no free cloud plan, only a 14-day trial. The free option is the self-hosted Community Edition.
Make pricing
| Plan | Price | Usage included | Good for |
|---|---|---|---|
| Free | $0 | 1,000 credits a month, 2 active scenarios, runs every 15 min at most | Testing an idea |
| Core | about $9–$12 per month | 10,000 credits, unlimited scenarios, runs every minute | Most small businesses |
| Pro | about $16–$21 per month | 10,000+ credits, priority runs, full-text log search | Busy single users |
| Teams | about $29–$38 per month | 10,000+ credits, team roles and permissions | Small teams |
| Enterprise | Custom | Custom | Large organisations |
The range on each paid plan is annual versus monthly billing. Higher credit volumes cost more on every tier.
Executions vs Credits: The Billing Difference That Decides Your Bill
This is the part most comparisons gloss over, and it matters more than the sticker price.
- n8n counts executions. One run of a workflow is one execution, whether it has 3 steps or 30.
- Make counts credits. Every module that runs uses a credit, including the trigger. A 10-step scenario that runs once uses about 10 credits. Loops and routers multiply this, because each pass and each active branch runs its modules again. (Make renamed “operations” to “credits” in 2025; for normal modules, one credit equals one old operation.)
So long workflows are cheap on n8n and expensive on Make, while short, simple workflows cost about the same on both.
Worked example: the same workflow on both
Say you capture leads from a web form, enrich them, score them with AI, add them to your CRM, and post a Slack alert. That is about 8 steps.
| Monthly runs | Make usage | Make plan needed | n8n usage | n8n plan needed |
|---|---|---|---|---|
| 300 | ~2,400 credits | Core (~$9–$12) | 300 executions | Starter ($20–$24) |
| 1,000 | ~8,000 credits | Core (~$9–$12) | 1,000 executions | Starter ($20–$24) |
| 3,000 | ~24,000 credits | Core with a bigger credit pack (well above the base price) | 3,000 executions | Pro ($50–$60) |
| 10,000 | ~80,000 credits | High credit tier | 10,000 executions | Pro ($50–$60) |
| Any volume | — | — | Unlimited | Self-hosted Community Edition (server cost only, roughly $5–$20 a month for a small VPS, plus your time) |
The takeaway: at low volume, Make is cheaper and easier. Once a multi-step workflow runs thousands of times a month, n8n’s per-execution billing (or free self-hosting) pulls ahead quickly.
Calling AI models inside either tool adds its own cost on top: on Make, native AI modules use extra credits based on tokens; on n8n, you pay your AI provider directly. Our AI API cost calculator helps you estimate that part.
AI Agent Support in 2026
Both platforms leaned into AI agents this year, but they took different approaches. n8n offers dedicated nodes for OpenAI, Claude, Gemini, and other models with direct parameter control, native agent-loop support, and vector database integration — built for teams constructing genuinely custom AI agent pipelines. Make added its own AI agent capabilities in 2026 too, narrowing the gap for users who want AI steps without leaving its visual builder.
The practical difference: if you’re building a bespoke, code-level AI agent workflow, n8n’s configurability goes deeper. If you want to add straightforward AI steps — summarizing, classifying, drafting — inside an otherwise visual workflow, Make’s newer AI features are increasingly capable without requiring custom code.
Integrations
Make wins clearly on the number of ready-made, pre-built connectors — a large, polished library covering popular marketing, sales, and productivity apps out of the box. If the app you need is on that list, Make gets you connected in minutes.
n8n has fewer native integrations, but its HTTP request node means a technical user can connect to essentially any service with a public API, official connector or not. That closes much of the gap for teams willing to do a bit of setup, but it’s a real gap for anyone who wants everything pre-built and doesn’t want to touch an API document.
Make vs n8n for Common Jobs
| Job | Better pick | Why |
|---|---|---|
| Send form leads to a CRM and Slack | Make | Pre-built modules, live in 15 minutes |
| AI agent that reads email and drafts replies | n8n | Native agent nodes, memory, any model |
| Sync two databases every minute | n8n | Per-execution billing keeps frequent runs cheap |
| Social media posting from a content calendar | Make | Ready-made connectors for most networks |
| Workflows touching sensitive client data | n8n (self-hosted) | Data stays on your own server |
| A non-technical team maintaining automations | Make | Easier to read and fix |
Switching Between Make and n8n
There is no import button between the two, so moving means rebuilding. A few tips if you are planning a switch:
- List every scenario or workflow with its trigger, apps and monthly runs. Move the most expensive ones first.
- Run both side by side for a week before turning the old one off.
- Watch credentials: reconnect each app with fresh OAuth logins rather than copying keys.
- Recheck the bill after a month. The worked example above is a guide, not a promise.
Not Sold on Either?
If neither fits, Zapier is the easiest of the three and has the most integrations, at a higher price per task. Our Zapier vs Make comparison covers that matchup, our roundup of the best Make.com alternatives compares six other options with real prices, and our list of free AI automation tools for solopreneurs includes a few lighter open-source options.
Which Should You Choose?
Match the tool to your team and situation rather than whichever one a YouTube video praised most recently:
- No developer on the team, want it working today — Make.
- Need to self-host for data control or compliance — n8n.
- High-volume or many-step workflows where cost matters — n8n, especially self-hosted.
- Want the widest pre-built connector library — Make.
- Building custom, code-level AI agent workflows — n8n.
- Prefer a fully managed tool with no server to maintain — Make.
For a lot of small teams, the pragmatic path is to start on Make’s free plan to prove the automation works, then evaluate self-hosted n8n later if volume or cost genuinely justifies the switch. For a closer look at how Make stacks up against the other major cloud player, our Zapier vs Make comparison covers that specific matchup in depth.
How SmartWorkflowLab Helps
We test automation platforms in real workflows, including the setup work that comparison articles often skip — provisioning a server for n8n, watching Make’s operation count climb on a real scenario, wiring up an actual AI agent node. That hands-on view is why our guidance centers on your team’s technical comfort and volume, not which tool has the flashier landing page.
If you’re deciding between n8n and Make for a specific project, or an existing automation is costing more than expected, that’s exactly the kind of practical problem we help work through.
Frequently Asked Questions
1. Is n8n or Make better in 2026?
It depends on your team. Make is better for non-technical users who want a fast, visual setup with the widest connector library. n8n is better for technical teams who need self-hosting, custom code, deep AI agent workflows, or lower costs at high volume. Neither is universally better — they serve different needs.
2. What is the main difference between n8n and Make?
n8n can be self-hosted for free and gives developers custom JavaScript and Python nodes for deep control. Make is cloud-only, more polished and visual, and easier for non-technical users to pick up. n8n trades ease of use for flexibility and cost control at scale.
3. Is n8n cheaper than Make?
At high volume, often yes, especially self-hosted, since n8n’s free Community Edition has no software cost beyond your own server. Make’s per-operation billing can climb quickly on complex or frequent workflows. At low volume, Make’s cloud plans can be simpler and cheaper to start with.
4. Do I need to know how to code to use n8n?
Not strictly, but it helps. n8n’s node-based builder works without code for many workflows, but its real power — custom JavaScript, HTTP requests, complex logic — assumes some technical comfort. Non-technical users generally find Make faster to learn from scratch.
5. Which has better AI agent support, n8n or Make?
n8n, for most technical use cases. It offers dedicated nodes for OpenAI, Claude, Gemini, and other models with direct parameter control and native agent-loop support. Make added its own AI agent features in 2026 too, but n8n’s AI integration is generally more configurable for custom agent workflows.
6. Can I self-host Make like I can with n8n?
No. Make is a cloud-only platform with no self-hosting option. n8n offers both a cloud-hosted service and a free, open-source version you can run on your own server, which matters for teams with strict data governance or private-data requirements.
7. Which has more integrations, n8n or Make?
Make has more pre-built, ready-to-use connectors out of the box. n8n has fewer native integrations but supports custom API and HTTP request nodes, so a technical user can connect to almost any service with an API, even without an official pre-built connector.
8. Should a small business choose n8n or Make?
For most small businesses without a developer on staff, Make is the faster, safer start thanks to its visual builder and large connector library. If you have technical capacity, expect to scale to high volume, or need to keep data on your own infrastructure, n8n’s self-hosted option is worth the steeper learning curve.
Final Thoughts
n8n versus Make isn’t a contest with one winner — it’s a fork based on who you are. Make hands you speed, polish, and a huge library of ready connectors, fully managed in the cloud. n8n hands you control, custom code, and a self-hosting option that pays off at scale, in exchange for a steeper learning curve.
Don’t pick based on which tool has louder fans this month. Pick based on whether your team has the technical comfort to run n8n, and whether your volume and data requirements actually need what self-hosting provides. If you’re still unsure, start on Make’s free plan, build your most important workflow, and see if you ever bump into its limits — that’s a better signal than any comparison, including this one.
Choosing an automation platform? SmartWorkflowLab tests these tools in real workflows and shares honest, no-affiliation verdicts. Explore our other automation guides, or get in touch if you want help picking or building the right setup.
