n8n vs Make (Integromat) for Enterprise Automation
Jantore S.8 min read
n8n wins for enterprises that need self-hosting, data sovereignty, and deep AI/LLM integration — especially in regulated markets like Kazakhstan. Make wins for teams that want managed simplicity and predictable visual workflows without infrastructure overhead. Choose n8n when compliance, code-level control, or AI-native automation matters. Choose Make when speed of deployment and low maintenance are the priority.
Head-to-Head Comparison
| n8n | Make | |
|---|---|---|
| Self-hosting & data sovereignty | Full self-hosting on your infrastructure. Docker, Kubernetes, any cloud or on-premise. Complete data sovereignty — nothing leaves your network. | Cloud-only. Data processed on Make servers (EU or US region). No self-hosting option. Some enterprise plans offer dedicated infrastructure. |
| AI/LLM integration capabilities | Native AI nodes for LangChain, OpenAI, Anthropic, Google Gemini, and local models. AI agent workflows, RAG pipelines, and vector stores built in. | Limited AI integration. HTTP module for API calls to LLMs, but no native AI workflow nodes, no agent frameworks, no vector store support. |
| Enterprise pricing & TCO | Community Edition is free and unlimited. Enterprise tier is seat-based. No per-operation charges — run millions of executions at fixed cost. | Operation-based pricing. Each action in a workflow costs an operation. High-volume workflows become expensive quickly. Enterprise plans available. |
| Workflow complexity ceiling | Code nodes (JavaScript/Python), sub-workflows, error branching, manual triggers, webhooks, cron. No practical ceiling for complex orchestration. | Visual-first design handles branching, iteration, and error handling well. Complex logic possible but constrained by visual paradigm — no raw code execution. |
| Developer experience | Built for developers. Code nodes, npm packages, custom node SDK, REST API, CLI. Version control via Git. Full programmatic control. | Designed for non-developers. Drag-and-drop interface excels at simple-to-medium workflows. Limited extensibility for custom logic beyond built-in modules. |
| Community & ecosystem | Open-source community with 400+ integrations. Active GitHub (45K+ stars). Self-hosted means you can build anything the API exposes. | Large module library (1,500+ app integrations). Strong template marketplace. Closed-source — community contributes scenarios, not platform code. |
Self-Hosting & Data Sovereignty
This is the decisive criterion for most enterprise evaluations in Kazakhstan and Central Asia. n8n runs on your servers — Docker container, Kubernetes cluster, or bare metal. Workflow data, credentials, and execution logs never leave your network. For companies subject to Kazakhstan's data localization requirements under the 2013 Personal Data Protection Law (amended 2023), this is not optional — it is mandatory. Make processes all data through its cloud infrastructure. While EU hosting is available, there is no option to run Make on-premise or within a Kazakh data center. For regulated industries — banking, telecom, energy, government-adjacent enterprises — this eliminates Make from consideration before any feature comparison begins.
AI/LLM Integration Capabilities
n8n has invested heavily in AI-native workflow capabilities. Its LangChain nodes allow you to build AI agent workflows — complete with memory, tool use, and retrieval-augmented generation — directly inside the automation canvas. Native nodes for OpenAI, Anthropic Claude, Google Gemini, and Ollama (local models) mean you can orchestrate LLM calls without writing HTTP request boilerplate. According to IDC, enterprise spending on generative AI platforms reached $19.4 billion in 2025, with workflow automation emerging as the primary integration surface. Make handles AI through its generic HTTP module — functional but manual. Every LLM call requires configuring headers, parsing JSON responses, and managing token limits by hand. There are no agent frameworks or vector store nodes.
Enterprise Pricing & TCO
Make charges per operation — every node execution in every workflow run counts against your quota. A 10-step workflow processing 1,000 items per day consumes 10,000 operations daily, or roughly 300,000 per month. At scale, this compounds fast. n8n Community Edition is free with no execution limits. You pay only for infrastructure — a single VPS at $20-50/month handles substantial workloads. Enterprise Edition adds SSO, RBAC, and audit logging at a per-seat license. Gartner's 2025 iPaaS market analysis notes that operation-based pricing is the primary driver of automation cost overruns, with enterprises routinely exceeding initial estimates by 3-5x once workflows scale beyond pilot phase. For enterprises planning hundreds of workflows, n8n's fixed-cost model is materially cheaper.
Workflow Complexity Ceiling
Every automation platform hits a ceiling — the point where the visual interface cannot express the logic you need. Make hits this ceiling at multi-level data transformation, recursive processing, and dynamic branching based on external state. You can work around it with routers and iterators, but the workarounds are fragile. n8n pushes the ceiling much higher through JavaScript and Python code nodes. When visual nodes are not enough, you write code — with full access to npm packages, environment variables, and execution context. Sub-workflows, error branches, manual approval gates, and webhook-triggered orchestration enable patterns that are impossible in purely visual tools. For enterprises building production-grade automation, the ceiling matters more than the floor.
Developer Experience
n8n is built for developers who automate, not marketers who connect apps. The code node gives you a full JavaScript or Python runtime. You can import npm packages, write unit-testable functions, and version-control your workflows as JSON files via Git. The CLI supports headless execution for CI/CD pipelines. The custom node SDK lets you build proprietary integrations and package them for your team. Make is built for citizen developers — and does that job well. The visual interface is polished, the module configuration is intuitive, and simple workflows ship fast. But when you need to parse nested JSON, transform data with custom logic, or integrate with an internal API that has no public module, you hit the limits of a no-code paradigm.
Community & Ecosystem
n8n's open-source model means the community contributes actual platform code — not just templates. With 45,000+ GitHub stars and an active contributor base, new integrations appear regularly from the community itself. The fair-code license (sustainable use) allows inspection, modification, and self-hosting while protecting the project commercially. According to Gartner's 2025 assessment of integration platforms, community-driven open-source tools are gaining enterprise share at 22% year-over-year, driven by transparency and extensibility demands. Make has a larger library of pre-built modules — over 1,500 app integrations compared to n8n's 400+. For teams that rely on plug-and-play connections without customization, Make's breadth is an advantage. But the closed-source model means you cannot inspect, modify, or self-host the platform itself.
Frequently Asked Questions
Yes. Self-hosted n8n runs entirely within your infrastructure, which means you control network policies, encryption at rest, access logs, and data retention. Enterprise Edition adds SSO via SAML/LDAP, role-based access control, audit logging, and execution log management. For companies subject to Kazakhstan's data protection regulations or industry-specific compliance frameworks (banking, telecom, energy), self-hosted n8n is the only workflow automation platform that allows full compliance without relying on a third-party cloud provider's certifications.
Make is the stronger choice for teams where the primary automation builders are non-technical — marketing, operations, or sales teams connecting standard SaaS applications. Its visual interface is more polished for simple workflows, and the 1,500+ pre-built modules reduce setup time. However, the gap narrows once workflows require data transformation, conditional logic, or custom API calls. At that point, teams typically need developer involvement regardless of platform — and n8n gives developers a significantly more capable environment to work in.
At enterprise scale, the difference is substantial. A Make Teams plan at $16,000 per year provides 800,000 operations per month. A 10-step workflow processing 5,000 items daily consumes 1.5 million operations monthly — already exceeding the plan limit and requiring overages or an upgrade. Self-hosted n8n Community Edition on a $40/month VPS handles the same volume with no execution limits. Even n8n Enterprise Edition with SSO, RBAC, and support is typically 40-60% less expensive than equivalent Make enterprise plans once workflows exceed a few hundred thousand operations per month.
n8n supports AI agent workflows with LangChain nodes — multi-step reasoning agents that use tools, maintain conversation memory, and retrieve context from vector stores. You can build RAG pipelines (retrieval-augmented generation) that connect your internal documents to an LLM, create classification workflows that route support tickets by intent, or orchestrate multi-model pipelines where Claude handles reasoning and a local model handles data extraction. Make can call AI APIs via its HTTP module, but it has no native agent framework, no vector store integration, and no built-in support for multi-step AI reasoning chains.
We run n8n in production for our own operations and for enterprise clients across Kazakhstan — contact forms, CRM pipelines, analytics digests, AI-powered proposal generators, and multi-step notification workflows. The platform comparison above comes from building real systems, not reading feature pages. If your enterprise is evaluating automation platforms and needs a recommendation grounded in your specific compliance, integration, and scaling requirements — reach out for a conversation.
Interested in working together? Contact us now