n8n vs Make
Both wire your GTM stack together, Clay, CRMs, enrichment, senders, and AI agents, but they price it very differently. n8n bills per workflow run and can be self-hosted; Make bills per step and runs fully managed in the cloud. Here is how they compare on cost, power, and AI.
By Kshitij Maheshwari, co-founder · Updated June 2026
Same job, a very different meter
Both connect your tools and move data between them. The split is who hosts it, how they charge, and how deep the AI goes.
Source-available and self-hostable, with the deepest AI-agent stack and a flat per-execution price, so a complex workflow costs the same as a simple one. Free to self-host, or from 20 euros a month in the cloud.
A polished visual builder with the widest app library and nothing to host, billed per step. The friendliest way for a non-engineer to wire many apps together, from a free plan up.
- ✓You have technical hands and want AI agents or custom code
- ✓You care about cost as workflows get complex
- ✓You want to self-host for data control
- ✓Your operators are non-technical and want a visual canvas
- ✓You want the broadest set of app connectors
- ✓You want it fully managed with nothing to run
Short on time? We'll tell you which fits your team and your stack.
What each tool actually is
n8n
A workflow automation tool that you can self-host or run in the cloud, with the deepest native AI-agent stack in the category. It bills per execution, meaning one full workflow run regardless of how many steps it has, so cost stays flat as workflows grow. Best for technical GTM teams building AI-heavy or high-volume automations who want data control.
Visit n8nMake
A cloud-hosted visual automation tool built for non-engineers, with the widest app library and a drag-and-drop canvas. It bills per operation, meaning each step or module call, so cost scales with how complex a workflow is. Best for lean GTM teams without an engineer who want to wire many apps together quickly.
Visit Maken8n vs Make, side by side
The facts that decide it, verified from each tool's official site in June 2026.
| Dimension | n8n | Make |
|---|---|---|
| Best for | Technical teams, AI agents, cost at scale | Non-technical operators wiring many apps |
| Hosting | Self-host or cloud | Cloud only |
| Open source | Source-available | No |
| Pricing meter | Per execution (whole run) | Per operation (each step) |
| Free plan | Yes, self-host or trial | Yes, 1,000 operations |
| App connectors | 400+ | 2,000+ |
| AI-agent nodes | 70+, full LangChain stack | Make AI Agents |
| Custom code | JavaScript and Python | JavaScript and Python |
| Entry paid price | 20 euros a month | About $9 a month |
| Learning curve | Steeper, code-friendly | Gentler, visual-first |
n8n prices in euros, Make in US dollars, and Make's pricing now uses a credit slider, so confirm current tiers on n8n and Make before you buy.
What each one can and cannot do
A capability check, scored the same way for both tools.
| Capability | n8n | Make |
|---|---|---|
| Visual builder | ✓ | ✓ |
| Self-host option | ✓ | ✕ |
| Open source | ✓ source-available | ✕ |
| Native AI-agent / LLM nodes | ✓ 70+, LangChain | ✓ AI Agents |
| Custom code (JS / Python) | ✓ | ✓ |
| Error handling and retries | ✓ | ✓ |
| Webhooks | ✓ | ✓ |
| 1,000+ app connectors | Limited ~400 | ✓ 2,000+ |
| Branching / router logic | ✓ | ✓ |
| On-prem / data residency | ✓ | Limited cloud only |
| Community templates | ✓ | ✓ |
| Git version control | ✓ | ✕ |
"Limited" means available but not a core strength. n8n closes its connector gap with a generic HTTP node and code, but that is manual work; Make is cloud-only, so true on-prem data residency is not an option.
What real users say
Public review scores and the themes that come up most, checked June 2026. Counts drift, so the live links are the source of truth.
n8n
Praised for: flexibility and code-level power, lower cost than cloud rivals especially self-hosted, and a deep AI-node stack.
Watch-outs: a steep learning curve for non-technical users, fiddly debugging, and self-hosting that adds maintenance.
Make
Praised for: an intuitive visual builder, a huge app library, and fast shipping of complex flows without code.
Watch-outs: per-operation costs that climb at scale, a 2025 billing change that raised effective cost for AI-heavy flows, and no self-host option.
Read the scores in context. Both sit high on G2 and Capterra across large samples, so the products are genuinely well-liked. The reviews mostly reflect who each is for: Make wins praise for ease, n8n for power and cost, and the most common Make complaint, runaway per-operation cost, is exactly the trade-off n8n's per-execution pricing avoids.
Where each one actually wins
Six things separate these tools in practice. Here is the honest call on each.
Pricing meter and cost at scale
Edge: n8nn8n charges per execution, so one workflow run is one unit no matter how many steps it has. Make charges per operation, so a multi-step enrich, score, route, and send pipeline can burn dozens of operations per run. For complex GTM workflows at volume, n8n's cost is far more predictable and lower, especially self-hosted.
Ease versus power
Edge: splitMake wins on approachability: a visual-first canvas a non-engineer can run. n8n wins on raw power: code, custom logic, and self-hosting. Which matters depends entirely on who is building and maintaining your automations.
AI-agent capabilities
Edge: n8nWith more than 70 AI nodes and a full native LangChain stack, agents, memory, tools, and vector stores, n8n is the stronger platform for production agentic workflows. Make AI Agents is real and improving, but n8n is deeper today.
Self-host and data control
Edge: n8nn8n can run entirely on your own infrastructure for full data ownership and residency, free on the Community Edition. Make is cloud only. For regulated data or strict residency needs, only n8n delivers it.
App connector breadth
Edge: MakeMake ships 2,000-plus app connectors against n8n's 400-plus, so more long-tail SaaS tools work out of the box. n8n can reach anything with HTTP and code, but that is extra setup, where Make is click-and-connect.
Support and community
Edge: tieBoth have large, active communities and template libraries. Make offers more structured tiered support on higher plans, while n8n's self-host support is do-it-yourself unless you pay, so the edge depends on your plan and appetite.
Automation wired, pipeline still flat?
Plumbing moves data. We decide who to target and run the outreach. Tell us your motion.
What each one costs in 2026
Verified from each official pricing page in June 2026. Read the seat model, not just the headline number.
n8n
EUR / per execution- Community
self-hosted, unlimited executions on your own serverFree - Starter
2,500 executions, unlimited steps20 euros/mo - Pro
10,000 executions, more concurrency50 euros/mo
Business runs 667 euros a month with self-host and Git version control, and Enterprise is custom. n8n bills per execution, a whole workflow run, not per step, so complexity does not raise the meter.
Make
USD / per operation- Free
1,000 operations a month, 2 scenarios$0 - Core
10,000 operations, unlimited scenariosabout $9/mo - Pro
more operations, priority executionabout $16/mo
Teams runs about $29 a month and Enterprise is custom. Make bills per operation, each module call, and switched its credit model in 2025, so confirm current rates on the live pricing page.
True cost at scale. Picture a normal outbound workflow: webhook in, dedupe, Clay enrich, AI score, CRM upsert, route, draft, send, about ten steps. On Make, running 10,000 leads a month is roughly 100,000 operations, pushing you into higher tiers and overage, and AI steps cost more. On n8n the same 10,000 runs is 10,000 executions, or unlimited if self-hosted. The more sophisticated the workflow, the wider that gap, which is the core reason technical teams pick n8n at scale.
What neither tool does well
Both are plumbing, so they share the same blind spots. Worth knowing before you expect either to carry your whole motion.
Both move and transform data, but neither creates it. You still need Clay, enrichment, or a database feeding them. They are the plumbing, not the well.
Both wire up senders and CRMs, but neither handles deliverability, inbox warmup, or sending reputation. Treating an automation tool as your sender is a mistake.
Both break quietly when an upstream API changes or a credential expires, and a stalled GTM workflow can quietly stall pipeline. Both need monitoring, and self-host adds infrastructure upkeep.
Want the targeting and timing that decides what those workflows act on? That is the signal-based outbound we run. Need the email-sending layer too? See our best cold email tools for 2026.
Our take, after running both
This is mostly about who builds and maintains your automations. Here is how we'd call it.
Per-execution pricing, the LangChain stack, and self-hosting reward a team that can own its automation layer and wants cost to stay flat as workflows grow.
The visual builder, the widest app library, and zero hosting let a lean team ship complex flows fast, as long as volume keeps per-operation cost in check.
Both move data between tools. Neither decides which accounts to target or what to say. That judgment is the part that builds pipeline, and it is on you, or on us.
Not sure which fits? We run signal-based outbound for early-stage teams and will tell you straight.
Book a Fit Check
Co-founder of Real Good GTM. He has been the first business hire and Chief of Staff at seed-stage B2B startups, building outbound pipeline before any playbook existed. This comparison comes from running these tools on live campaigns, not from a spec sheet.
Connect on LinkedInQuestions buyers ask
Is n8n or Make better for GTM automation?
Why is Make more expensive at scale than n8n?
Can I self-host n8n or Make?
Which is easier to learn, n8n or Make?
Which has more integrations, n8n or Make?
Which is better for AI agents and workflows?
Can n8n or Make run my outbound for me?
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Not sure which fits your motion?
Book a fit check. We'll look at your stack, your team, and how you sell today, and tell you straight which tool, or which setup, actually fits.
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