n8n vs Make (Integromat): Workflow Automation Compared (2025)
n8n and Make are the two most popular visual workflow automation platforms. Both are rule-based. Both break on exceptions. Here's an honest comparison, including pricing, integrations, self-hosting, and what to do when you outgrow them.
n8n and Make (formerly Integromat) are the two most commonly compared visual workflow automation platforms. n8n is open-source, developer-focused, and self-hostable. Make is cloud-only, more accessible to non-developers, and broader on pre-built integrations. Both handle multi-step workflows, conditional logic, and API connections. Both are genuinely different tools — and both share the same fundamental ceiling.
This article covers where each tool wins, where each struggles, and what to consider when both hit their limit.
Side-by-side comparison
| Dimension | n8n | Make (Integromat) |
|---|---|---|
| Architecture | Open-source, fair-code licensed | Proprietary, cloud-native |
| Builder | Visual node editor | Visual scenario editor |
| Self-hosting | Yes (full infrastructure control) | No (cloud-only) |
| Integrations | 1,200+ nodes (source) | 3,000+ modules (source) |
| AI capabilities | AI Agent nodes with LangChain, memory nodes | AI Agents added April 2025; OpenAI/LLM modules |
| Target user | Developers and technical teams | Technical non-developers, operations teams, agencies |
| Error handling | Error triggers, retry logic, fallback branches | Error handlers per module, break/resume/rollback |
| Pricing | Free community self-hosted; cloud from $20/mo (source) | Free tier (1,000 ops/month); paid from $9/mo (source) |
| Billing unit | Per workflow execution (not per step) | Credits-based as of August 2025 (was per-operation) |
| Community | 50,000+ GitHub stars (source) | Large user base, active community |
| Enterprise features | SSO, audit logs, Git version control, dedicated support | SSO, custom domains, priority support |
| Execution model | Event-driven + scheduled | Scenario-based with scheduling |
| Data handling | JSON-based, code nodes for transforms | Built-in data mapping, iterators, aggregators |
Where n8n wins
Self-hosting and infrastructure control
This is n8n's most significant differentiator. If your organization requires on-premise deployment, has strict data residency requirements, or wants to own the automation infrastructure end-to-end, n8n gives you that. Make is cloud-only — there is no self-hosted option.
For enterprises with specific compliance regimes or government data requirements, this matters practically. Self-hosting also means you can inspect the codebase, customize n8n's behavior, and integrate it deeply with internal infrastructure.
One important caveat: self-hosting is free in software licensing but not in infrastructure costs. Production-grade self-hosted n8n deployments typically run $50–200/month depending on workload and the cloud provider used, once you account for compute, storage, and maintenance overhead (analysis).
Fair-code license — and what it actually means
n8n uses its own Sustainable Use License (a fair-code model), not a standard open-source license. The code is publicly available and you can inspect it fully, but commercial use has restrictions: you may use or modify n8n only for internal business purposes. Redistributing or providing n8n to third parties as a service requires the Enterprise license.
For security-conscious organizations that want source-code transparency, this still delivers the core benefit: you can audit exactly what the tool does with your data. Make is proprietary and provides no equivalent visibility.
Developer flexibility
n8n is built for developers. Code nodes let you write JavaScript or Python inline within any workflow step. The node SDK lets you build custom integrations. The workflow execution model — event-driven, JSON-native, API-first — is straightforward for engineers to reason about. If your team works in terms of webhooks, API calls, and data transformations, n8n fits that mental model natively.
AI agent nodes
n8n has integrated LangChain-powered AI Agent nodes that let you add LLM reasoning to workflows: classification, extraction, summarization, decision routing. With memory nodes and tool-use routing, n8n has built out closer to 70 AI-focused nodes as of 2025, making it the most AI-capable option in this category (comparison).
Make introduced AI Agents in April 2025, catching up functionally, though n8n's AI integration is more mature and deeply wired into the workflow architecture.
Important clarification: adding an AI node to a rule-based workflow does not change the underlying architecture. The orchestration remains predefined. Agent nodes do not maintain persistent memory across separate workflow executions by default. The workflow itself cannot hold a sustained conversation or make autonomous decisions that span multiple separate runs.
Where Make wins
Integration breadth
3,000+ modules vs. 1,200+ nodes. Make has significantly more pre-built integrations. For teams that rely on niche SaaS tools, this difference is practical. When the integration you need does not exist natively in n8n, you are configuring HTTP request nodes with manual API setup. In Make, a native module likely exists (Make integrations).
Business user accessibility
Make's scenario builder is more accessible to non-developers. Visual data mapping between modules is intuitive. Data transformations happen through a point-and-click interface rather than code expressions. Error handling is configured per module with clear options: break, resume, ignore, rollback. For teams where the people building automations are operations or marketing professionals rather than engineers, Make has a meaningfully lower barrier to entry.
Built-in data handling
Make's data model is more opinionated and user-friendly out of the box. Iterators, aggregators, routers, and data transformations are first-class visual components rather than code. For scenarios involving arrays, nested data structures, or multi-branch routing, Make's approach is faster to implement for non-developers.
No infrastructure overhead
Make is fully managed. No servers to provision, no updates to apply, no scaling to handle, no security patches to deploy. For teams without DevOps capacity, this eliminates an entire category of operational work. Updates and scaling happen automatically. You focus on building scenarios, not maintaining infrastructure.
Pricing at entry level
Make's free tier (1,000 operations/month) and low starting price make it more accessible for small teams experimenting with automation. n8n's cloud option is competitive, but self-hosted n8n, while free in licensing, carries real infrastructure costs for any production deployment. As of August 2025, Make has moved to a credits-based billing system — complex or AI-heavy modules consume more credits, so teams should model their specific usage before assuming the entry price holds at scale (Make pricing).
Which companies use n8n vs Make in production
n8n is widely used by engineering-led teams and technical operations. Typical production use cases include: internal developer tooling and CI/CD integrations, data pipeline automation with custom transformation logic, security and compliance workflows requiring self-hosted infrastructure, and companies in regulated industries (finance, healthcare, government) with data residency requirements.
Make is widely used by agencies, growth teams, and operations-heavy companies. Typical production use cases include: marketing automation across multiple SaaS platforms, client reporting pipelines, e-commerce order processing and fulfillment routing, and no-code-first teams automating across 5+ SaaS tools without engineering support.
Both see heavy adoption in SMBs and scale-ups. Enterprise adoption of both tools tends to be departmental rather than org-wide, with IT-approved platforms (Workato, MuleSoft, Boomi) used for core system integrations.
Where both tools hit the same wall
Here is the part most comparison articles skip. n8n and Make are different tools with real trade-offs. But they share the same fundamental limitation — and it is the limitation that matters most for enterprise use cases.
Both are rule-based
Every workflow in both platforms follows predefined logic. If Condition X, execute Action Y. If Error Z, run Fallback W. Every possible scenario must be anticipated in advance and wired as a path. On the defined path, both tools are sturdy. Off the path, both tools break.
Both fail on exceptions that require judgment
When a customer sends a message that does not match expected formats, both tools error out or route to a human. When a support ticket involves a nuanced situation the workflow designer did not anticipate, both tools stop. When data is ambiguous and the right action depends on context that exists outside the workflow, both tools have no mechanism for judgment.
Both cannot hold conversations
Neither tool can interact with a customer or employee through a natural conversation, interpret intent, ask clarifying questions, and take action based on full context. n8n's AI Agent nodes add LLM reasoning to individual steps — this is real capability. But the workflow itself cannot maintain a conversation across executions. Context does not persist between separate workflow runs by default. Each execution starts from scratch.
Both create the brittleness tax
Every new exception requires a new branch. Every API change requires a rewire. Every edge case the designer did not anticipate requires human intervention. This maintenance scales linearly with workflow complexity. Teams that start with five clean workflows typically end up with fifty branching workflows that nobody fully understands, with a growing share of team time going to monitoring and fixing rather than the work those workflows were meant to automate.
Both leave most enterprise automation potential untouched
The workflows both tools handle well — structured triggers, clean data, predictable paths — represent a fraction of enterprise automation potential. The larger portion involves ambiguity, judgment, conversations, multi-system decisions, compliance interpretation, and exception handling. That portion stays manual because rule-based automation architecturally cannot reach it.
Decision framework: n8n vs Make
| Your situation | Choose n8n | Choose Make |
|---|---|---|
| Infrastructure control matters | Yes — self-hosting required | No — cloud is fine |
| Team is developers | Yes | Not necessarily |
| Data residency requirements | Yes | Check Make's data region settings |
| Need open-source transparency | Yes (Sustainable Use License) | Not a priority |
| Integration breadth matters | 1,200+ nodes sufficient | Need 3,000+ modules |
| No DevOps capacity | Use n8n Cloud, not self-hosted | Yes — Make is fully managed |
| Budget-sensitive, no infra team | n8n Cloud from $20/mo | Make free tier or from $9/mo |
| Need AI nodes in workflows | More mature AI node library | AI Agents added April 2025 |
| Non-developers are building | Steeper learning curve | Yes — more accessible UI |
| Billing predictability | Per-execution (predictable) | Credits-based since Aug 2025 (model your usage) |
n8n and Make alternatives for enterprise automation
The comparison above helps you choose between n8n and Make. But if you are reading this because your current automation is hitting its ceiling, the right next step might not be switching from one rule-based tool to another.
The signs are consistent:
- Workflows break on exceptions, and fixing them has become a recurring job
- The processes that actually matter to the business cannot be automated because they require judgment
- AI nodes help with individual steps but do not solve the end-to-end problem
- Maintenance time is approaching or exceeding the time the automation saves
- Business teams depend on developers to build and maintain every workflow
When these patterns converge, the problem is not which visual workflow builder you are using. It is the category.
Beyond rule-based automation: intelligent workflow agents
Nexus is an autonomous agent platform paired with Forward Deployed Engineers. It is a different category from both n8n and Make. Where workflow tools follow predefined rules, Nexus agents combine process execution with conversational intelligence and autonomous decision-making. They handle the structured path and the exceptions. They hold conversations with customers and employees. They make judgment calls within defined guardrails. They adapt when systems change without requiring a complete rebuild.
What it looks like in production:
-
Orange Group (multi-billion euro telecom, 120,000+ employees): Business team deployed autonomous customer onboarding agents across multiple European markets in 4 weeks. The previous workflow automation handled the structured path but broke on non-standard requests, missing data, and ambiguous inputs. Result: 50% conversion improvement, ~$6M+ yearly revenue impact, 100% team adoption.
-
European telecom (13,000+ employees): Workflow automations existed for support operations — sturdy for simple routing, broken when compliance entered the picture. 40% support volume freed with Nexus agents. Full regulatory compliance maintained. 12-week deployment.
Key differences from n8n and Make:
| Dimension | n8n / Make | Nexus |
|---|---|---|
| Architecture | Rule-based, predefined logic | Autonomous agents with decision-making |
| Exception handling | Errors out, routes to human | Reasons through exceptions autonomously |
| Conversations | Cannot hold sustained conversations | Conversational intelligence built in |
| Who builds | Developers / technical users | Business teams (no code required) |
| Integrations | 1,200–3,000 | 4,000+ enterprise integrations |
| Maintenance | Linear with complexity | Agents adapt, no rebuild required |
| Compliance | Depends on your setup | SOC 2, ISO 27001, ISO 42001, GDPR native |
| Support model | Self-serve / community | Forward Deployed Engineers embedded in your team |
| Pricing | Per-execution / per-credit | Per-agent, tied to value delivered |
The gap between workflow automation and autonomous agents is not a feature gap. It is a category gap. You cannot close it by switching from n8n to Make or Make to n8n.
Worth exploring?
If your n8n or Make workflows are working well for structured tasks, keep using them. They are good tools for what they do.
If you have hit the ceiling — exceptions keep breaking flows, maintaining brittle workflows is becoming its own job, the processes that matter most require judgment and conversations that rule-based automation cannot provide — it may be worth seeing how the shift works.
Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see results before committing.
See the full Nexus vs n8n comparison →
Frequently asked questions
What is the main difference between n8n and Make?
n8n is open-source, self-hostable, and designed for developers and technical teams who want infrastructure control and code flexibility. Make is cloud-only, more accessible to non-developers, and offers a larger library of pre-built integrations (3,000+ vs 1,200+). n8n charges per workflow execution; Make uses a credits-based model as of August 2025.
Is n8n better than Make for enterprise use?
It depends on what "enterprise" means for your organization. If you need self-hosted deployment, data residency guarantees, or source-code auditability, n8n is the stronger choice. If you need rapid deployment across many SaaS tools without DevOps capacity, or your automation team is primarily non-technical, Make is more practical. Both tools have genuine enterprise customers but are typically used for departmental automation rather than org-wide core system integration.
Can n8n be self-hosted for free?
Yes, under n8n's Sustainable Use License (a fair-code model), the community edition can be self-hosted at no software cost for internal business use. Infrastructure costs apply and typically range from $50–200/month for a production-grade deployment. Commercial redistribution or providing n8n as a service to third parties requires the Enterprise license. Make has no self-hosted option.
How many integrations does Make have compared to n8n?
Make offers 3,000+ pre-built modules as of 2025. n8n offers 1,200+ nodes. Both support HTTP request nodes for any REST API not covered natively. Zapier, the category leader by integration count, offers 6,000+. For niche SaaS tools, Make's breadth is a practical advantage over n8n.
When should I use n8n vs Make vs Zapier?
Use n8n when your team includes developers, you need self-hosting or data residency, you want maximum flexibility with code nodes, or you are building AI-augmented workflows. Use Make when your team is non-technical, you need a large library of native integrations out of the box, and you want a fully managed platform with no infrastructure overhead. Use Zapier when you need the broadest integration coverage (6,000+) and the lowest technical barrier — at the cost of higher per-operation pricing at scale. All three are rule-based and share the same limitation: they cannot handle judgment-requiring exceptions autonomously.
Related reading
- Nexus vs n8n: full comparison
- Top 10 n8n alternatives for workflow automation
- Top 10 open-source automation tools for enterprise
- Nexus vs Zapier: rule-based automation vs intelligent agents
- How to choose a workflow automation platform
- How to automate business workflows with AI agents
- Zapier alternatives for enterprise automation
