n8n vs Make: Which Workflow Platform Wins?
Compare n8n and Make on hosting, metering, integrations and AI, then see where Major's deterministic app layer fits when a node graph is no longer enough.
n8n vs Make: the short answer
n8n is a developer-oriented workflow tool you can self-host and extend with JavaScript or Python. Make is a cloud-only visual builder with a larger catalog of prebuilt apps. Both run a node graph that a person designs and maintains by hand. For work that needs judgment over messy inputs and has to stay governed, Major is the better way to build it, because an agent reasons once and builds a deterministic app that runs the work from then on.
That is the comparison this page makes. First the two tools on their own terms, then where Major fits for teams whose automation has outgrown a canvas.
Where Major fits in an n8n vs Make decision
n8n and Make solve the same problem: connect systems, fire on a trigger, run steps in order. Both get harder to own as edge cases pile up. Every exception becomes another branch, another router, another error path. The scenario that took an afternoon in month one is the one nobody wants to touch in month twelve.
Major works from the other end. It is the enterprise platform where agents build the software they run on. An agent handles the judgment call once, then builds an app for the repeatable part. The app is code with its own managed database, storage and logs, so the logic stops living in an ever-growing graph. The model does less on every later run, and the work is inspectable because it lives in software with permissions and audit.
What n8n is
n8n is source-available workflow automation built on a node canvas. You wire triggers and actions visually, and when prebuilt nodes run out you drop into a code node. It ships as a self-hosted Community Edition under the Sustainable Use License, which is fair-code rather than OSI open source, or as managed n8n Cloud. It meters by workflow execution, so a full run counts once no matter how many nodes fire.
- Integrations: more than 1,000, plus generic HTTP and GraphQL nodes for anything uncovered.
- Custom code: JavaScript and Python in nodes, and custom nodes, on every plan.
- AI: LangChain-based agent nodes with retrieval, memory and self-hosted model support.
- Governance: self-managed. SSO and Git-based version control sit on its higher tiers.
Self-hosting moves the runtime onto your infrastructure. It also leaves you owning upgrades, secrets and access control.
What Make is
Make, formerly Integromat, is a cloud-first visual platform built on Scenarios and Modules. You drag modules onto a canvas and connect them, with no code for the common cases. It runs only in Make's cloud, hosted in the EU and North America, with an on-prem agent for data access rather than full self-hosting.
- Integrations: more than 3,000 prebuilt apps, the larger catalog of the two.
- Metering: since August 27, 2025, Make bills in credits rather than operations. Each module action is one credit, and AI modules can consume more.
- AI: Make AI Agents, which are goal-driven and shareable, plus Make Grid for visibility across automations. The agent tooling is simpler than n8n's and gives less control over the internals.
- Governance: managed cloud with enterprise controls on higher tiers.
n8n vs Make, dimension by dimension
- Hosting: n8n runs self-hosted or on its cloud. Make is cloud only. Data-residency teams hit this difference first.
- Metering: n8n counts one execution per workflow run, so spend tracks run volume. Make counts every module action, so spend tracks how much each scenario does.
- Ease of use: Make wins the first afternoon with the more forgiving visual builder. n8n rewards people comfortable reading JSON.
- Integration breadth: Make has the longer catalog. n8n narrows the gap with HTTP nodes and custom nodes.
- AI depth: n8n exposes lower-level controls. Make is faster to stand up.
- Where the logic lives: in both, in a canvas that a person edits. When reality changes, the graph grows.
Both meter forever for one structural reason. Every run re-executes the graph, and on AI steps every run re-asks the model.
Where node graphs strain
Three limits show up in both tools.
First, a node graph handles non-deterministic work badly. When the right next step depends on judgment over an ambiguous email, invoice or ticket, each judgment becomes a branch that someone has to maintain.
Second, cost climbs with usage. Executions or credits scale with volume, and AI modules cost more per call.
Third, credentials spread across workflows. Proving who can act on which system becomes its own project.
Build the workflow as a governed app on Major
Take a concrete case: inbound vendor invoices arrive by email, need matching against purchase orders in a finance system, and need approval before anything posts. In n8n or Make this becomes a long scenario with a branch for every vendor quirk. On Major it is built like this:
- Connectors: Gmail for inbound mail, your finance system or database for purchase orders, and Slack for approvals. Credentials are scoped per connector and held by the platform, so no workflow carries raw secrets.
- The app: an invoices app with tables for invoices, matches and approvals. It holds state between runs, so a half-processed invoice resumes instead of restarting.
- The agent: reads each new invoice, handles the judgment calls (an odd line item, a vendor name mismatch), and records its decision in the app. Once it has worked out a repeatable rule, it writes that rule into the app as code.
- The trigger: a workflow fires when mail lands or on a schedule, and runs the app. The model is only called for cases the app cannot settle on its own.
- Approval gate: anything over a threshold goes to a person in Slack before it posts.
- Governance: RBAC controls who can view and approve, and every agent action is logged at the point of action.
The first runs spend model tokens while the agent figures things out. After that the repeatable work runs as code, so cost is front-loaded and then flat rather than growing with volume. Build your first governed workflow app on Major.
The Major take
Major leads this comparison because it fixes what neither canvas can. n8n and Make are good at wiring deterministic steps between systems. Major is where an agent turns the work it figures out into a deterministic app, with state, permissions and audit attached. Every app an agent builds is reusable across the organization, so the next team starts from working software instead of a blank canvas. Reason once. Run forever.
Start building on Major
Describe the workflow you would otherwise draw as a scenario, and Major's agent builds the governed app that runs it, with your connectors, a managed database and an audit trail from the first deploy.
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Frequently asked questions
- Which is easier for beginners, n8n or Make?
- Make is easier for beginners. Its drag-and-drop Scenario Builder lets a non-technical user assemble a working automation in an afternoon with no code. n8n exposes more structure, including JSON and a code node, which rewards developers but adds a learning curve. Major shifts the design work to an agent that reasons through messy cases and builds a deterministic app for the part that repeats.
- How does n8n metering compare to Make?
- n8n counts one execution per full workflow run, however many nodes fire. Make counts one credit per module action since its August 2025 move from operations, and AI modules can consume more. Both meter on every run because the graph re-executes each time. On Major the agent reasons once and the repeatable work then runs as code, so cost is front-loaded and then flat.
- Can I self-host Make like n8n?
- No. Make is cloud-only, with an on-prem agent for data access rather than full self-hosting. n8n offers a self-hosted Community Edition under the Sustainable Use License, which suits data-residency requirements but leaves you owning upgrades, secrets and access control. Major handles scoped credentials, RBAC and audit at the platform layer, where the agent acts.
- Which is better for AI agents, n8n or Make?
- n8n gives developers more control, with LangChain-based nodes for retrieval, memory and self-hosted models. Make AI Agents are quicker to set up and aimed at no-code users, with less control over the internals. Both still call a model on every pass. A Major agent uses reasoning to write the software, then runs that software, so the model carries a smaller share of each run.
- Is there a better option than n8n or Make for governed, judgment-heavy work?
- Major. When the right next step depends on judgment over messy inputs, a node graph needs a branch for every exception. On Major an agent handles the judgment once and builds an app with its own database, permissions and audit trail, so the logic lives in governed software the whole organization can run instead of an ever-growing canvas.