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GTM tools · Comparison

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


The 30-second verdict

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.

n8n is the power and ownership pick

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.

Make is the no-code, fully-managed pick

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.

Pick n8n if
  • 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
Pick Make if
  • 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
Book a 30-min fit check

Short on time? We'll tell you which fits your team and your stack.


The basics

What each tool actually is

n8n

Source-available, self-hostable automation

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 n8n

Make

Visual cloud automation

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 Make

At a glance

n8n 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.


Feature checklist

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.


Ratings & reviews

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.


The deciding factors

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: n8n

n8n 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: split

Make 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: n8n

With 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: n8n

n8n 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: Make

Make 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: tie

Both 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.

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Pricing

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 server
    Free
  • Starter
    2,500 executions, unlimited steps
    20 euros/mo
  • Pro
    10,000 executions, more concurrency
    50 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 scenarios
    about $9/mo
  • Pro
    more operations, priority execution
    about $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.


The honest gap

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.

Sourcing the data

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.

Sending the outreach

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.

Failing loudly

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.


How we'd choose

Our take, after running both

This is mostly about who builds and maintains your automations. Here is how we'd call it.

1
Technical team building AI agents, pick n8n

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.

2
No engineer, want it managed, pick Make

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.

3
Either way, automation is plumbing, not strategy

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

Kshitij Maheshwari, co-founder of Real Good GTM
About the author
Kshitij Maheshwari

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.

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FAQ

Questions buyers ask

Is n8n or Make better for GTM automation?
Neither is better outright, it depends on your team. n8n is stronger for technical teams that want AI agents, custom code, self-hosting, and flat per-execution pricing as workflows grow complex. Make is stronger for non-technical operators who want a polished visual builder, the widest app library, and nothing to host. Match the tool to who will build and maintain it.
Why is Make more expensive at scale than n8n?
They charge differently. Make bills per operation, meaning each step in a workflow, so a ten-step pipeline run on 10,000 leads can be around 100,000 operations a month. n8n bills per execution, meaning one whole workflow run, so those same 10,000 runs are 10,000 executions, or unlimited if self-hosted. The more steps your workflows have, the wider that gap gets.
Can I self-host n8n or Make?
You can self-host n8n but not Make. n8n's Community Edition is free and runs on your own server with unlimited executions, which gives you full data ownership and residency. Make is cloud-only with no self-host option. If keeping data in your own environment matters, that difference alone points to n8n.
Which is easier to learn, n8n or Make?
Make, for most people. Its visual canvas is designed for non-engineers, and you can wire apps together without code. n8n is more powerful but steeper, and it rewards comfort with APIs, JSON, and a bit of code. If your team is non-technical, Make gets you live faster; if it is technical, n8n's depth pays off.
Which has more integrations, n8n or Make?
Make, by raw count. Make ships more than 2,000 app connectors against n8n's 400-plus, so more long-tail SaaS tools work out of the box. n8n can still reach almost anything through its generic HTTP node and code, but that takes extra setup. For click-and-connect breadth, Make leads; for build-anything flexibility, n8n does.
Which is better for AI agents and workflows?
n8n, today. It has more than 70 native AI nodes and a full LangChain stack with agents, memory, tools, and vector stores, which makes it the stronger platform for production agentic workflows. Make has launched AI Agents and native LLM modules and is improving, but n8n is the deeper AI-orchestration tool right now.
Can n8n or Make run my outbound for me?
No. Both connect your tools and move data, but neither sources leads, decides who to target, or sends and warms email. They are the plumbing between a data source and a sender, not the strategy or the campaign. We handle the targeting and timing on real signals and use automation tools like these to wire it together.

Keep exploring

More from Real Good GTM

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