Jantore S.11 min readn8n 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. It is worth asking the prior question first, though: a good share of the workloads that arrive at this comparison belong in your own codebase or a scheduled job rather than on either platform.
| 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. |
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.
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. Enterprise adoption of generative AI is accelerating rapidly: according to Menlo Ventures' 2025 State of Generative AI in the Enterprise report, companies spent $37 billion on generative AI in 2025, up from $11.5 billion in 2024 — a 3.2x year-over-year increase — 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.
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. The gap widens at volume: a Make Teams plan at $16,000 per year provides 800,000 operations per month, while self-hosted n8n on a $40/month VPS handles multiples of that with no execution limits. For enterprises planning hundreds of workflows, n8n's fixed-cost model is materially cheaper.
The comparison above is the one every evaluation runs. The more useful question turned out to be the one before it: does this workload belong on an automation platform at all?
opengate self-hosts n8n and has run production workflows on it since 2024 — form intake, a weekly analytics digest, notification routing, and a language-model-backed proposal generator. Several of those have since moved off n8n, and none of them moved because n8n failed.
Form intake moved into the application itself. The website's contact and newsletter forms originally posted to an n8n webhook, which wrote to a spreadsheet and sent the acknowledgement. That works. But those forms also needed rate limiting, an origin allowlist, a submission-timing gate and a CAPTCHA check — and all of that logic already existed in the application, in the same language, deployed on the same push. Keeping the sink in n8n meant validation lived in one system and the write lived in another, with a network hop between them and two places to look when a lead went missing. The route runs inline now. n8n was not the wrong tool; it was a second deployment target for logic that only needed one.
The proposal generator moved for a different reason. It orchestrated a language model to produce a document, and each step's instruction was a paragraph of prose rather than an operation. Once that is true, expressing the sequence as connected nodes adds ceremony without adding clarity — the node graph shows you the order of steps you already knew and hides the wording that actually determines the output. It runs as a scheduled job in CI now, against the same repository it writes into.
What stayed on n8n is what n8n is genuinely best at: scheduled, multi-service, side-effect-heavy sequences that touch four or five external APIs, each with its own credential and retry behaviour, and that someone other than the author will eventually need to read.
The most expensive lesson from running n8n in production has nothing to do with Make, and it applies to any workflow platform: a green execution is not evidence that anything happened.
One of our workflows wrote leads into a spreadsheet. A column header in that sheet carried a trailing space — "Name " rather than "Name". n8n stores a snapshot of the sheet's schema inside the node configuration, and when the stored column names stop matching the live headers, the write fails. Submissions were accepted at the webhook, processed without an error anyone was watching for, and never landed. The execution list did not look obviously wrong. The spreadsheet simply stopped gaining rows.
A later n8n release changed that behaviour, and a schema mismatch now raises a loud top-level error rather than failing quietly. That is the right fix and a good argument for staying current. But the general lesson outlives the specific bug, and it is now the rule we apply to every automation on every platform: the check is the data sink, never the run status. Read the row. Count the rows. Confirm the message arrived. A wall of green ticks is a report on the orchestrator's opinion of itself.
This lands harder on a self-hosted platform than on a managed one, and it is the honest cost side of the self-hosting argument. You own the upgrade cadence, so you also own the class of bug a vendor would otherwise have patched underneath you.
After moving several workloads in one direction and deliberately keeping others in place, the rule we ended up with is short, and it decides more than the n8n-versus-Make question does.
A workflow belongs on an automation platform when it is scheduled rather than user-triggered, when it touches several external services that each need their own credential and retry behaviour, and when somebody other than its author will one day need to see what it does. That last condition is the one people undervalue: a canvas is documentation that cannot go stale, and for a sequence that fires monthly, being readable a year later beats being elegant.
A workflow belongs in your application when a user triggers it, when it shares validation or business logic with code that already exists, or when its correctness is worth a test. Automation platforms are awkward to unit-test, and a lead-capture path that can silently drop submissions is worth a test.
A workflow belongs in a scheduled job when its steps are instructions rather than operations — long prose, judgement, generated artefacts. Prose does not decompose into nodes without losing the thing that made it prose.
Neither n8n nor Make helps with this decision, which is why an evaluation that starts at the feature table often ends up automating something that should have been forty lines inside the codebase.
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. The broader iPaaS market reflects this shift: Gartner's July 2025 Market Share Analysis found the iPaaS market grew 23.4% to $8.5 billion in 2024, driven in part by rising demand for extensible, developer-controlled platforms. 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.
No, and this is the question worth settling before comparing platforms. A workflow belongs on an automation platform when it is scheduled rather than user-triggered, when it touches several external services that each need their own credential and retry behaviour, and when someone other than its author will need to read it later. It belongs in your application when a user triggers it, when it shares validation or business logic with code that already exists, or when its correctness is worth a test — automation platforms are awkward to unit-test. And it belongs in a scheduled job when its steps are instructions rather than operations, because long prose does not decompose into nodes without losing what made it prose. We moved several workloads out of n8n on exactly these grounds, and none of them moved because n8n failed.
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.
opengate self-hosts n8n and runs production workflows on it for its own operations — scheduled analytics digests, multi-service notification routing, and internal orchestration — and has moved other workloads off it deliberately, which is where the material above comes from. The comparison is written from operating a platform and then retiring parts of it, not from 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.
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