TL;DR verdict: Make is the easier, cheaper way to start automating visually — $9/month gets you 10,000 credits and a builder anyone on the team can read. n8n is the better long-term buy for technical users: it charges per full workflow run instead of per step, its AI agent features run deeper, and the self-hosted edition is free with unlimited executions. Non-technical team = Make. Technical builder = n8n.
Bottom line: Make charges per module, n8n charges per run — that one difference decides most purchases.
Make vs n8n at a glance
| Feature | Make | n8n | Edge |
|---|---|---|---|
| Free option | Free plan: 1,000 credits/mo, 15-minute minimum run interval | Community Edition: self-hosted, free, unlimited executions | n8n |
| Entry paid price | Core $9/mo annual ($10.59 monthly) for 10,000 credits | Cloud Starter €20/mo annual (€24 monthly) for 2,500 executions | Make |
| Mid tier | Pro $16/mo annual; Teams $29/mo annual (each 10,000 credits base) | Cloud Pro €50/mo annual (€60 monthly) for 10,000 executions | Depends on workflow shape |
| Billing unit | Credits — each module that runs costs a credit | Executions — one full workflow run costs one, however many steps it has | n8n for long workflows |
| Hosting | Cloud only | Cloud or self-hosted | n8n |
| Custom code | Limited; built for no-code visual building | Full JavaScript and Python code nodes | n8n |
| AI features | AI modules and scenario building help | AI agent nodes, RAG pipelines, vector stores, custom LLM chains | n8n |
| Ease of use | Visual scenario builder most non-developers can follow | Node-based; steeper learning curve | Make |
Prices checked October 16, 2026 from current pricing pages and pricing guides. n8n prices are listed in euros. Research-based comparison, not hands-on testing.
Why the billing unit matters more than the sticker price
A five-step Make scenario that runs 1,000 times spends roughly 5,000 credits. The same workflow in n8n spends 1,000 executions, because a run is a run no matter how many nodes it contains. At low volumes Make’s $9 Core plan is cheaper in absolute dollars. As workflows get longer and busier, n8n’s per-run math — or a free self-hosted install on a $5–$10/month server — pulls away. Always model your busiest workflow before choosing.
Where Make pulls ahead
Approachability and price of entry. Make’s canvas shows the whole scenario at once, branching and all, and operations staff can usually maintain what they build. The free plan is genuinely usable for slow, simple jobs, and Core removes the painful 15-minute scheduling floor for less than a team lunch. If nobody on your team wants to own a server or write code, this is the safer home.
Where n8n pulls ahead
Depth. Code nodes, self-hosting, Git-based version control on higher tiers, and the most advanced AI automation toolkit of the mainstream builders — agent loops, retrieval pipelines, and bring-your-own-model chains — make n8n the tool developers grow into rather than out of. The catch is ownership: self-hosting means your team handles updates, backups, and security, and cloud prices jump steeply from Pro to the self-hosted Business tier at €667/month annual.
Which should you pick?
Pick Make if you want automations running today, your team is non-technical, and your workflows are short to medium length. Pick n8n if you build long or AI-heavy workflows, want your data on your own infrastructure, or your execution volume makes per-step billing hurt. Agencies and technical freelancers tend to land on n8n; small business ops teams tend to land on Make.
Frequently asked questions
Is n8n really free?
The self-hosted Community Edition is free with unlimited executions — you pay only for your server. n8n Cloud is paid, starting at €20/month on annual billing.
Is Make cheaper than n8n?
At entry level, yes: $9/month versus €20/month. At high volume with long workflows, n8n usually wins because it bills per run, not per step — and self-hosting removes the per-run cost entirely.
Which is better for AI agents?
n8n. Its agent nodes, vector-store integrations, and code access support more complex AI workflows than Make’s module-based approach.