n8n and Make are the two tools people reach for when Zapier runs out of power — and they solve it from opposite ends: n8n is open-source, self-hostable, and code-friendly; Make is visual, cloud-only, and no-code. Your technical capability and data-control needs usually decide it.
Key takeaway: Choose n8n to self-host, control your data, write code, or build AI-agent workflows. Choose Make for maximum no-code power and the broadest library of prebuilt integrations. Neither is an AI-employee platform — both run workflows you configure.
n8n and Make (formerly Integromat) are the two tools people reach for when Zapier is not powerful enough. Both build complex, multi-step automations with branching, logic, and data handling that go well beyond simple trigger-action flows. But they solve the "I need more power" problem from opposite directions: n8n comes from the open-source, self-hosted, developer-friendly world, while Make comes from the visual, cloud-first, no-code world. This guide covers what actually matters.
If you are comparing against the easier option too, read this alongside Make vs. Zapier and Zapier vs. n8n.
Quick verdict
Choose n8n if you want to self-host, control your data, build AI agent workflows, or write real code inside your automations, and you have technical capability. Choose Make if you want maximum power without touching code, broad prebuilt integrations, and no infrastructure to manage. n8n wins on control, code, and cost at scale; Make wins on approachability and prebuilt breadth.
The fundamental difference
n8n: open-source, self-hostable automation
n8n is an open-source workflow tool you can self-host on your own infrastructure or run on their managed cloud. It pairs a visual builder with real code (JavaScript or Python) inside workflows, native AI agent nodes, and full control over where your data lives. The trade-off is that power and self-hosting come with a steeper setup.
Philosophy: give technical teams full control and no vendor lock-in, without giving up the visual builder.
Make: visual, cloud-only automation
Make is a cloud platform built around a large visual canvas. Its routers, iterators, and aggregators let non-developers build genuinely complex scenarios by dragging modules, not writing code. It runs entirely on Make's infrastructure. The trade-off is that you cannot self-host, and heavy logic still runs inside a no-code frame.
Philosophy: make complex automation visual and approachable, so you do not need to be a developer to build it.
Feature comparison
Integrations
n8n offers 400+ built-in integrations plus an HTTP request node that connects to any API, so nothing with an API is off-limits if you are willing to configure it. Make offers 1,500+ polished, prebuilt modules plus a generic HTTP module, giving broader out-of-the-box coverage.
Winner: Make for prebuilt breadth, n8n for connecting anything yourself.
Workflow complexity
n8n handles loops, branching, merges, sub-workflows, error handling, and custom code inside the workflow, and you can drop into code when the visual layer runs out. Make has strong visual logic through routers, iterators, and aggregators, with a lower ceiling only when you need real custom code.
Winner: Roughly even; n8n if you want code and self-hosting, Make for max power without code.
AI capabilities
n8n has native AI agent nodes and LangChain integration, with support for OpenAI, Anthropic, and local models, so you can build full AI agent pipelines inside a workflow. Make has a growing set of AI modules and AI-assisted scenario building, capable for AI steps but less oriented toward building custom agents.
Winner: n8n for building real AI agents, Make for adding AI steps.
Pricing
Self-hosting the open-source version of n8n is free for the software; you pay only for a server, usually a few dollars a month, and their managed cloud is subscription-based. Make is cloud-only, priced per operation, with a free tier of around 1,000 operations a month.
Winner: n8n at scale via free self-hosting, Make for a managed cloud tool.
Data control and ease of use
Self-hosted n8n keeps all workflow data on your own infrastructure, which matters for regulated industries or data-residency needs, but the learning curve is real and self-hosting adds work. Make runs in the cloud and is the more approachable of the two, with a visual canvas non-developers can learn.
Winner: n8n for data control, Make for ease of use.
Side-by-side
| Factor | n8n | Make |
|---|---|---|
| Integrations | 400+ built-in, any API via HTTP | 1,500+ prebuilt modules |
| Workflow complexity | Advanced (code, sub-flows) | Advanced (routers, iterators) |
| AI capabilities | Strong (AI agents, LangChain) | Growing (AI modules) |
| Self-hosting | Yes (free, open-source) | No (cloud only) |
| Data control | Self-host keeps data in-house | Cloud only |
| Pricing | Free self-hosted; cloud by executions | Per operation, free tier ~1,000 |
| Ease of use | Moderate learning curve | Very visual, approachable |
| Best for | Technical teams, AI pipelines, data control | No-code teams, complex visual scenarios |
In practice: the same workflow, two tools
You want an automation that scrapes data, runs it through an AI agent for decisions, and keeps everything on your own servers.
With n8n, you self-host, use the AI agent nodes and custom code, and no data ever leaves your infrastructure, ideal for control and AI-heavy logic.
With Make, you build a strong visual scenario quickly with no infrastructure, but the data runs through Make's cloud and building a true agent is harder.
The pattern: n8n wins on control, code, and AI agents, Make wins on approachable, no-code power.
Best use cases
Reach for n8n when you are:
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Required to self-host or control data
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Building AI agent workflows or writing code in automations
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Technical, and want low cost at high volume
Reach for Make when you are:
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Non-technical and want complex automation without code
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After broad, polished prebuilt integrations
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Happy to let a cloud tool handle hosting
Limitations to keep in mind
Neither is a true agent platform: both run configured workflows and cannot adapt like an AI agent on their own. n8n's power comes with a learning curve and self-hosting overhead; Make cannot keep data in-house and needs custom code for the deepest logic. Pricing and features change, so verify current details.
What both share (and lack)
n8n and Make are both workflow automation tools. They connect apps and run sequences you configure. They are powerful plumbing, but plumbing, not personnel. Neither gives you AI employees with business roles, a shared brain that agents read from, or a workspace where AI and human work converge. Each workflow runs alone, with no shared knowledge.
The alternative approach
If your goal is not "automate this workflow" but "get AI teammates that own a function," that is a different category: an AI employee platform. Agently provides AI employees for sales, operations, marketing, support, and research in a shared workspace, reading from one brain and acting across your tools. And through Agently's MCP server, custom agents you build in n8n can join that workspace and share the same context. n8n and Make automate workflows. Agently provides the workforce.
Bottom line
Choose n8n for self-hosting, data control, AI agent pipelines, custom code, and low cost at scale.
Choose Make for a visual, no-code way to build complex automations with broad integrations.
Look beyond both if you want AI that works as part of your team, with business roles, a shared brain, and one workspace where AI and human work come together.
Agently provides AI employees that work alongside your team in a shared workspace, not just automating workflows but handling sales, marketing, operations, support, and research. Try it free.
