n8n vs Make (2026): Which Should Your Business Actually Use?
n8n and Make are both serious automation platforms that handle far more complexity than Zapier. The deciding factor is straightforward: does your team have a developer? n8n delivers self-hosting, infrastructure control, and native code execution for technical builds. Make gives everyone else a powerful visual builder with managed infrastructure and a newer code option when visual modules are not enough. We have built with both and the distinction is consistent.
Bottom Line
Use n8n if you have technical resources and want full data control or need to run custom code in workflows. Use Make if your team works without dedicated engineering resources and needs more workflow power than Zapier provides.
Both platforms handle complex workflows well. The real differentiator is who maintains them. An n8n workflow with JavaScript logic nodes requires someone who can read and edit code. A Make scenario with router modules and data transformers is accessible to non-technical operators, even though Make Code now covers advanced JavaScript and Python cases on paid plans. For a SaaS company with an engineering team, n8n's self-hosted cost structure is compelling: fewer vendor constraints and data can stay on your servers. For an agency or e-commerce brand with a non-technical ops team, Make's visual canvas and managed infrastructure are worth the monthly cost.
n8n vs Make: Overview
n8n
Self-hostable automation with full developer control
n8n is a source-available workflow automation tool that gives technical teams control over their automation infrastructure. Self-hostable and developer-friendly, it supports JavaScript and Python natively and connects to any API via its HTTP Request node. Its LangChain-backed AI nodes support LLM agents, RAG, vector stores, memory, and AI pipelines.
Best For
Technical teams, developers, data-sensitive businesses, high-volume automation
Pricing
Community self-hosted option / Cloud from EUR20/mo annual for 2.5K full workflow executions.
Pros
- +Self-hostable with full data ownership
- +Self-hosted Community Edition avoids per-task billing
- +JavaScript and Python natively inside nodes
- +Connect to any API via HTTP Request node
- +Purpose-built LangChain and AI agent nodes
- +Active developer community
Cons
- –Requires technical setup and maintenance for self-hosted
- –Steeper learning curve
- –Fewer native integrations than Make
- –UI less intuitive for non-developers
Make
Visual no-code automation for complex workflows
Make is a visual automation platform with a canvas-based builder that makes complex workflows easy to understand and maintain. It offers strong data transformation tools, generous credit-based pricing, 3,000+ apps, and Make Code for JavaScript and Python on paid plans.
Best For
Visual builders, non-technical teams, complex workflows with light code, cost-sensitive teams
Pricing
Free tier (1,000 credits/mo) / Paid from $9/mo. Credit-based pricing scales affordably.
Pros
- +Visual canvas showing your full data flow at a glance
- +Strong built-in data transformation tools
- +No technical setup required
- +Generous free tier
- +Cost-effective credit-based pricing
- +3,000+ native integrations
- +Make Code supports JavaScript and Python on paid plans
Cons
- –No fully self-hosted option
- –Make Code is still beta and requires a paid plan
- –Can be slow on large data sets
- –Cloud platform, though higher tiers can use an on-prem agent for local networks
Feature Comparison
| Feature | n8n | Make |
|---|---|---|
| Self-hosting | Yes, full controlWin | No, cloud platform |
| Pricing at scale | Self-hosted infra cost / Cloud executionsWin | Credit-based, low cost |
| Technical requirement | Developer recommended | No-code capableWin |
| Code in workflows | JavaScript + Python nodesWin | Make Code on paid plans |
| Visual workflow canvas | Basic | ExcellentWin |
| Data transformation | Via code nodes | Built-in visual tools |
| Native integrations | 1,000+ | 3,000+Win |
| Data privacy | Full, self-hosted optionWin | Cloud platform + on-prem agent |
| AI agent support | LangChain nodes + agent toolsWin | AI apps + Make Code |
| Setup time | Longer, especially self-hosted | QuickWin |
Voltaris Agency Note
From hands-on experience building with each platform
For technically demanding builds (custom code, data privacy controls, or a developer in the loop), n8n is our default. For builds that need to be owned and maintained by non-technical operators, Make is the right call. Both platforms produce excellent production-grade automations. The real cost to account for is the ongoing maintenance burden on your team, not the tool subscription.
Common Questions
Is n8n better than Make for complex automations?
It depends on what complex means. For workflows that require custom code, API manipulation, or data transformations beyond standard mapping, n8n is more capable. For workflows that are visually complex with many branches, conditional paths, and loops across multiple modules, Make's canvas often makes them easier to build and maintain without deep technical support.
How much does n8n cost compared to Make?
n8n has a self-hosted Community Edition, so you pay for your server infrastructure, typically $5-20/mo on a VPS for small setups. n8n Cloud starts at EUR20/mo billed annually for 2.5K full workflow executions. Make's paid plans start at $9/mo for 10,000 credits. For low-to-medium volume, Make is comparable in cost. For high-volume automation, self-hosted n8n usually has the stronger cost profile.
Can I switch from Make to n8n?
There is no official importer between the two platforms. Workflows need to be rebuilt manually. For technically capable teams, rebuilding a Make scenario in n8n is usually straightforward. The effort is typically worthwhile for anyone running significant automation volume who wants more control over infrastructure and long-term cost.
Which platform is better for AI agent workflows?
n8n. Its LangChain implementation includes agent, chain, vector store, embedding, memory, tool, and model nodes that fit complex AI pipelines. Make supports AI tools through native AI apps, HTTP modules, and Make Code, which is useful for many workflows. For deeper agent and RAG orchestration, n8n's purpose-built AI tooling is still stronger.
Do automation agencies use n8n or Make?
Both. The split depends on the build's technical requirements. At Voltaris, we reach for n8n on technically demanding builds and where data sensitivity is a requirement, and Make when the workflow needs to be maintainable without developer support.
Want to get the right tool in place?
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