n8n vs Make: The Honest 2026 Comparison | 2V Automation

If you’re choosing between n8n and Make (formerly Integromat) in 2026, the short answer is this: Make wins on visual clarity and consumer-app breadth, n8n wins on cost-at-scale, code, AI, and self-hosting. The right pick depends on how much logic you need to wire together and where the work runs.

Both tools sit in the same “more powerful than Zapier, friendlier than raw code” middle ground. They diverge sharply on pricing model, deployment options, and how technical the user needs to be. This is the full head-to-head.

The 60-second summary

Choose Make if you want a clean visual canvas, your team is comfortable with SaaS-only tooling, your workflows are moderately complex but not enormous in volume, and you want a friendlier on-ramp than n8n.

Choose n8n if you’re past a few thousand workflow runs a month, you need self-hosting, you want real code, you’re building AI agents, or you’re already paying Make uncomfortable amounts.

Now the long version.

What each tool actually is

Make

Make started life in 2016 as Integromat, rebranded in 2022 after the Celonis acquisition. It’s a managed SaaS automation platform built around a visual canvas. You drag modules (each one a single operation against an app) onto a scenario, draw lines between them, and the scenario runs on a trigger or schedule.

The product’s signature is its diagram-style UI: every module is a labeled circle, routes branch visibly, iterators and aggregators are explicit shapes. For a certain kind of operator - visual thinkers, marketers, people who like seeing the flow - it’s the cleanest interface in the category.

The unit of billing is the operation: every time a module does something (a fetch, a write, an API call), that’s one operation. Operations are billed against a monthly cap.

n8n

n8n launched in 2019 in Berlin. It’s a source-available, node-based workflow engine under the Sustainable Use License (which replaced the earlier fair-code license in late 2022). You can use the managed n8n Cloud or self-host the Community Edition on your own infrastructure.

The interface is also a visual canvas - n8n is closer to Make than to Zapier in feel - but the underlying model is more data-first. Each node receives and emits arrays of items. You can write JavaScript or Python inline in a Code node. Workflows are JSON, so they live cleanly in Git. The same engine runs on your laptop, a $20/month VM, or a Kubernetes cluster with queue mode and dozens of workers.

For the deeper backgrounder on n8n itself, see our n8n automation guide.

Pricing: per-operation vs per-execution

The single most important structural difference between these tools.

Make’s pricing model is per operation. A scenario that pulls 500 records from Airtable, transforms each, and writes 500 rows to Postgres burns at minimum 1,500 operations - one for each module-invocation per record. Iterators and aggregators add their own ops. Plans cap monthly operations, and overage charges kick in past the cap.

n8n Cloud’s pricing model is per workflow execution. That same workflow processing 500 records is one execution, not 1,500. A single execution can touch thousands of records and still count as one.

n8n self-hosted has no usage-based pricing at all. You pay for the VM. That’s it.

What this means in practice. Suppose you process 100,000 records a month across your workflows:

• Make: depending on shape, that’s 200,000-500,000 operations. Mid-tier plan range.
• n8n Cloud: could easily be a few hundred to a few thousand executions, depending on workflow batching. Entry-to-mid tier.
• n8n self-hosted: zero in software. A $30/month VM handles it.

For an honest dollar-comparison framework, our workflow cost calculator takes your specific numbers and prints a side-by-side. At low volume, Make and n8n Cloud are roughly comparable. At meaningful operations volume, n8n’s per-execution model is structurally cheaper, and self-hosted is in a different league.

Also see our deeper breakdown in n8n pricing in 2025.

Integrations

Make wins on raw count. Roughly 1,800+ first-party apps. The catalog covers most of the consumer and SMB SaaS world - every project tool, every marketing platform, every productivity tool, plus the long tail of niche apps. If a tool exists, Make probably has it.

n8n wins on universality of the catch-all. The HTTP Request node hits any API, and credentials are first-class so you can authenticate properly. n8n’s first-party integration list is around 500 - every system serious operations depend on (Postgres, MySQL, MongoDB, AWS services, HubSpot, Salesforce, Stripe, Notion, Airtable, Slack, Google Workspace, Microsoft 365) - plus a deep set of AI-specific nodes.

In practice: if your workflow touches a niche consumer SaaS, Make probably has the pre-built module. If it touches an internal database, a cloud service, or any of the standard business stack, both tools cover you, and the HTTP node closes the gap on anything else.

Branching, logic, and complex workflows

This is where the two tools differ in philosophy.

Make’s logic primitives are routes (parallel paths), filters (conditional gates on a path), iterators (turn an array into a stream of items), and aggregators (collapse a stream back into a single bundle). It’s visually elegant - you can see the shape of your data transformation at a glance.

The limits: Make’s data model is one item at a time inside an iterator. If you need to do bulk operations against an array, you’re using iterator/aggregator pairs everywhere, which racks up operations fast. Re-merging routes mid-scenario isn’t always clean. Sub-scenarios exist but are less flexible than n8n’s sub-workflow calls.

n8n’s primitives are IF, Switch, Merge, Split In Batches, sub-workflow invocations, and a per-item data model that handles arrays natively. Every node operates on an array of items by default, so bulk operations are the cheap case, not the expensive one. Workflows can call other workflows, errors can route to a dedicated error workflow, and the canvas supports loops back to upstream nodes.

If your workflows are mostly linear with a couple of branches, Make’s diagram-style canvas is genuinely more readable than n8n’s. The moment you need real fan-out, fan-in, error handling, and sub-workflow composition, n8n is the better fit.

Custom code

Make has limited inline JavaScript through the “Tools” module. You can write small transforms, but you can’t pull in arbitrary npm packages, you can’t call internal services, and there’s no native Python. For most users this is fine; if you need actual code, it’s a ceiling.

n8n lets you write full JavaScript or Python in a Code node. Pull in libraries, build helpers, call internal APIs. Inline expressions in any field for quick logic. And for recurring needs, you can ship a TypeScript custom node to your instance - packaged the same way as built-ins. We’ve written about exactly when your business needs its own n8n node.

If you’re never going to write code, this gap doesn’t matter. If your workflows are likely to outgrow no-code at some point, n8n’s runway is much longer.

Error handling

Make has scenario error handlers - you can attach an error route to any module, and you can configure scenarios to disable themselves after N consecutive failures. The execution log shows you what failed and where. Replays work cleanly.

n8n has the same per-node “Continue on Fail” toggle (so the workflow keeps running with the error captured as data) plus a first-class error workflow concept: any production workflow can route its failures to a separate workflow that gets the full context - which workflow, which node, what error - and can post to Slack, page on-call, write an incident ticket, whatever.

Both tools are competent at error handling. n8n’s separate error-workflow pattern is the better fit if you’re running dozens of production workflows and want a single observability path for failures across all of them.

Scalability and performance

Make scales by tier. Higher tiers get more operations, faster scenario runs, more active scenarios, and more team seats. The infrastructure is Make’s responsibility. Performance is generally good but you can’t tune it; if you outgrow the highest tier, you’re stuck.

n8n scales by your infrastructure. Self-hosted in queue mode with Redis and multiple worker processes, n8n handles millions of executions per month. We run client instances doing exactly that. The bottleneck becomes Postgres and your worker pool, both of which you control. n8n Cloud has higher tiers that match the load curve too.

At low and moderate volume both are fine. At high volume, n8n is the only one of the two whose economics remain sustainable.

AI features

Both tools have shipped AI features over the past two years. n8n’s stack is materially more developed for serious AI work.

Make’s AI offering includes OpenAI, Anthropic, and Google modules for individual model calls, plus the Make AI Agents capability (which lets you build agents on the canvas with module-calling tools). It’s improved meaningfully over the last year. For straightforward “let me add an LLM to my scenario” use cases, it’s well-suited.

n8n’s AI offering is more substantial. Native nodes for OpenAI, Anthropic, Google Gemini, Mistral, Groq, Ollama (local models), and more. A real LangChain integration with first-class nodes for vector stores (Pinecone, Qdrant, Supabase, PGVector), embedding generators, memory systems, structured output, and tool calling. Any existing workflow can be exposed as a tool that an agent calls, so your existing automation library becomes the agent’s toolbox.

For RAG systems, multi-tool agents, document extraction at scale, or anything that needs to integrate with internal models or vector databases, n8n is the more capable foundation today. If you want to go deeper, see our AI automation guide and our breakdown of what RAG is and how to use it.

Version control and team workflows

Make has in-app scenario revisions and a “Make DevOps” feature on higher tiers that supports environments and team workspaces. There’s no native Git integration; you can’t diff scenarios in a PR.

n8n workflows are JSON. Export, commit, diff, review in a PR, promote between environments. n8n Enterprise has native environments; Community Edition users get the same effect with Git plus a deploy script. For an engineering-driven team building many workflows, this matters.

Learning curve

Make is somewhere between Zapier and n8n on difficulty. The visual canvas is welcoming, the module library is large, the diagram style is genuinely intuitive for visual thinkers. A non-technical operator can be productive in a day or two for moderately complex scenarios.

n8n is steeper if you’ve never used a workflow tool before. The data model (per-item arrays, expressions, the Code node) takes a week of hands-on building to internalize. If you’re coming from Make, the visual concept transfers directly; you’ll be productive in a day or two.

Where Make wins (being fair)

• Visual clarity. The diagram-style canvas is the cleanest in the category. Make scenarios are genuinely more readable at a glance than the equivalent n8n workflow.
• Consumer-SaaS breadth. ~1,800 apps including the long tail Zapier covers and n8n doesn’t. If you need a niche consumer module, Make probably has it.
• Easier on-ramp for non-developers. The data model is less alien than n8n’s per-item arrays.
• No infrastructure thinking required. SaaS only, but for many teams that’s a feature, not a limitation.
• Native iterators and aggregators. The way Make models “loop over a list” is visually explicit and easy to teach.

Where n8n wins

• Cost at scale. Often 5-10x cheaper at meaningful volume, especially self-hosted.
• Real code. Full JS/Python, custom nodes, no sandbox.
• Self-hosting. Data residency, compliance, internal-only systems, air-gapped environments.
• AI capability. Native agent builder, vector stores, tool calling, local models.
• Production operations. Queue mode, error workflows, observability, Git-based versioning.
• No execution caps. Self-hosted has no software-imposed ceiling.
• Per-execution pricing. A single workflow can touch thousands of records at no extra cost.

When to migrate from Make to n8n

Strong signals you’re ready:

• Your monthly Make bill is past $200 and growing
• You’re hitting operation caps and adding tiers feels like a tax
• You have workflows that loop heavily over arrays (each iteration burns ops)
• You need to keep data inside your infrastructure
• You’re building AI features beyond a single model call
• You have engineering capacity to maintain a Node.js application (or you’re moving to n8n Cloud)

We’ve written the full step-by-step in how to migrate from Zapier or Make to n8n.

When Make is the right choice

Stay on Make if:

• Your scenarios are moderately complex but volume is moderate
• Your team prefers a strongly visual canvas
• You need the long tail of consumer SaaS integrations
• You’re SaaS-comfortable and don’t need self-hosting
• You’re under a few hundred dollars a month on the bill

How to decide

A simple decision tree:

1. Is your team strongly visual and your workflows moderately complex? Make. The canvas alone justifies it.
2. Are you under 50,000 operations a month with simple-to-moderate scenarios? Either works. Pick by team preference.
3. Are you over 200,000 operations a month, or do your workflows loop heavily over arrays? n8n. The per-execution model changes the economics.
4. Do you need self-hosting or air-gapped deployment? n8n. Make doesn’t offer it.
5. Are you building serious AI features? n8n. The LangChain stack and agent builder are ahead.

Or run your specific numbers on the workflow cost calculator.

Related reading

• n8n automation: the complete guide
• n8n vs Zapier: the complete comparison
• n8n vs Make vs Zapier: which automation tool wins?
• How to migrate from Zapier or Make to n8n
• Make vs Zapier: which automation tool wins?
• n8n pricing 2025
• Workflow cost calculator

If you’re trying to decide whether to migrate, stay, or build something new - and where automation actually pays back first in your business - our Efficiency Scorecard is the fastest answer. 15 minutes, free, you keep the output regardless of whether we work together.