n8n vs Make (2026): Which Automation Platform Should You Use?

n8n and Make are two of the strongest automation platforms in 2026—but they’re built around very different philosophies.

• Make (formerly Integromat) is optimized for visual workflow design, operational monitoring, and cost-effective cloud automation at high volume.
• n8n is optimized for flexibility, developer workflows, and—most importantly—self-hosting.

If you’re trying to decide between them, the right choice comes down to: your team’s technical skill, your compliance needs, and how you want to pay for automation (operations vs executions vs infrastructure).

This guide compares pricing, features, scalability, AI support, and real-world use cases.

Quick Verdict

Choose Make if you want the best visual builder, predictable cloud pricing per operation, and strong tooling for complex branching scenarios.

Choose n8n if you want self-hosting, code-level customization, and an automation layer that can become part of your engineering stack.

Best hybrid approach (common in 2026): Make for business ops + n8n self-hosted for engineering/infrastructure workflows.

Core Differences (In One Table)

Pricing Comparison (2026)

Pricing is where decisions often get made—especially at scale.

n8n pricing (billed annually, as displayed Feb 2026)

*Unlimited from n8n’s side, but limited by your infrastructure.

Make pricing (typical 2026 tiers)

What’s the real difference between “executions” and “operations”?

• Make operations: every module step counts. A 10-module scenario typically costs 10 ops per run.
• n8n executions: one workflow run counts once (as the pricing metric), regardless of step count. (You still pay in time/compute for self-hosted.)

Practical implication:

• If your workflows are step-heavy, n8n can be cheaper (in cloud) for a given number of runs.
• If your workflows are run-heavy (many triggers), Make can be easier to scale cheaply because operations are priced very low.

Example cost model: 10,000 runs/month

Assume a workflow runs 10,000 times/month.

• n8n Cloud Pro: €50/mo includes 10K executions.
• Make Pro: $16/mo includes 20K operations, but if each run uses 10 ops, you need 100K ops → you’d need a higher plan or additional operations.

If your workflow is step-heavy, n8n’s execution model can win.

Example cost model: 100,000 step-actions/month

Assume you need 100,000 “unit actions” in total.

• Make: priced for operations; 100K ops is affordable relative to many competitors.
• n8n Cloud: depends on how those actions map to executions. If it’s 100K executions, cloud pricing rises quickly.

If your workload is trigger-heavy, Make often wins.

Workflow Builder & Developer Experience

Make’s visual scenarios (best-in-class flowchart UI)

Make’s biggest advantage is its visual clarity:

• routers and filters for branching
• aggregators and iterators for data handling
• strong debugging via live execution view

For many teams, Make is simply faster to build and maintain complex workflows because the UI is so good.

n8n’s node-based editor + code steps

n8n feels more like a tool built for engineers:

• code steps (JS/Python)
• global variables
• API control
• Git-based version control (on higher tiers / self-host patterns)

If your team already thinks in “pipelines,” n8n fits naturally.

Key difference:

• Make excels at visual complexity.
• n8n excels at programmatic complexity.

Integrations & Extensibility

Make: 3,000+ pre-built integrations

Make has broad app coverage and is especially strong in:

• e-commerce
• marketing SaaS
• productivity tools

It also supports HTTP modules for custom APIs.

n8n: smaller catalog, but API-first

n8n has fewer “one-click” integrations than Make, but often compensates with:

• strong HTTP/API tooling
• custom nodes
• code-based transformations

If you can read API docs, you can integrate almost anything.

AI Automation Capabilities (2026)

AI workflows are a major reason teams revisit their automation stack in 2026.

Make AI capabilities

Make supports major AI platforms as integrations (commonly via prebuilt modules), and it’s excellent for:

• content generation pipelines
• enrichment + classification flows
• multi-step AI processing

n8n AI capabilities

n8n is frequently used to build AI agent “orchestration” because:

• you can chain tools via HTTP
• you can store/retrieve context
• you can add code and custom logic

n8n Cloud also includes AI Workflow Builder credits, which can accelerate building workflows from prompts.

Bottom line:

• Make is often faster to build “AI pipelines for business ops.”
• n8n is often better for “AI pipelines as infrastructure.”

Reliability, Monitoring & Debugging

Make monitoring

Make is famous for:

• detailed execution logs
• a “watch it run” visual debugger
• strong operational visibility

If your automation is a critical business process, Make’s ops tooling is a real advantage.

n8n monitoring

n8n includes execution logs, search, and debugging features, but production-grade monitoring is often better when self-hosted with:

• centralized logging
• alerting (e.g., Prometheus/Grafana)
• external observability tooling

Bottom line: Make offers great monitoring out of the box; n8n can be world-class with proper infra.

Security, Compliance & Data Control

This is where n8n can be the decisive winner.

n8n

• Self-hosting enables strict data residency
• You can keep secrets and logs inside your environment
• Suitable for regulated industries when deployed correctly

Make

• Cloud-only means you’re trusting the vendor’s environment
• Enterprise features can help, but you still don’t get full data locality like self-hosting

If you have requirements like: “data cannot leave our VPC,” n8n is often the only viable choice.

Pros & Cons

n8n Pros ✅

• Self-hosting option
• Code steps and custom nodes
• Execution-based pricing model can be efficient for complex workflows
• Strong fit for engineering teams

n8n Cons ❌

• Cloud plans can be limiting for high-trigger workloads
• Smaller integration catalog
• Self-hosting adds operational burden

Make Pros ✅

• Best visual workflow builder
• Excellent value per operation
• Strong debugging and operational visibility
• Large integration catalog (3,000+)

Make Cons ❌

• No self-hosting
• Operation-based pricing can penalize step-heavy workflows
• Collaboration/governance strongest on higher tiers

Use Case Recommendations

Choose Make for:

1. E-commerce ops: order processing, inventory sync, fulfillment logic
2. Marketing ops: lead routing, campaign reporting, content workflows
3. Agencies: managing many client workflows
4. Ops-heavy companies: high-volume, multi-step, visual routing

Choose n8n for:

1. Engineering automation: GitHub/Jira pipelines, internal tooling
2. Compliance workflows: sensitive data, regulated sectors
3. API-heavy integrations: custom SaaS, proprietary services
4. AI orchestration: agent pipelines, tool-calling workflows

Migration Notes (Make ↔ n8n)

There’s no one-click import between platforms. Expect to rebuild.

Migrating Make → n8n

Best when:

• you hit compliance requirements
• you need code-level control
• you want to consolidate “automation + custom logic”

Plan 2–8 hours per workflow depending on complexity.

Migrating n8n → Make

Best when:

• you want faster visual iteration
• non-technical teams own automation
• you want better out-of-the-box monitoring

Plan 1–6 hours per workflow.

Final Recommendation

If you want one platform

• Most business teams: choose Make.
• Most engineering-led teams: choose n8n (self-hosted).

If you want the best outcome

Use both:

• Make handles high-volume, business-owned workflows.
• n8n handles sensitive data and engineering automation.

That division matches how many modern companies operate in 2026: business automation is a product line, and engineering automation is infrastructure.

Last Updated: February 10, 2026

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• Make Review 2026
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• Make vs Zapier