We've shipped 80+ workflow automation builds on both. Here's our honest breakdown by reliability, hiring pool, and total cost of ownership.
Both tools work. The question isn't which is 'better' — it's which fits the team that has to live with it after the agency hands it over. We've shipped 80+ production workflows on Make and n8n combined, and the answer depends on three things: who maintains it, what it integrates with, and how much custom logic lives inside.
Where Make wins
- Time-to-first-automation: a junior ops person can ship something useful in an afternoon.
- Error notifications and visual debugging that non-engineers actually understand.
- Largest pool of freelancers who can take over — important when the agency leaves.
- Native connectors to the SaaS stack most service businesses already pay for.
Where n8n wins
- Developer ergonomics: real version control, real code nodes, real branching.
- Self-hosting on a $20 VPS — useful for HIPAA, GDPR, and UAE PDPL compliance.
- Custom integrations against internal APIs without paying per-operation pricing.
- AI agent nodes that don't require duct-taping three tools together.
The total cost of ownership trap
Make's operation pricing is brutal at scale. Once a workflow runs 50,000+ operations a month, the per-operation cost crosses self-hosted n8n breakeven. But the moment you self-host, you own uptime, backups, and patching. That's a hidden $400–800/month engineering cost most SMBs aren't honest with themselves about.
Our default recommendation
For service businesses without an in-house tech lead: Make. The visual debugger and the freelancer pool make it survivable after handoff. The moment there's a Python-fluent ops engineer on staff, or compliance requires self-hosting, we migrate to n8n.
Hybrid is fine
Some of our best builds run both: Make for the customer-facing flows (forms, CRM sync, SMS) and n8n for the back-office AI agents that need custom Python and longer-running jobs. Don't pick a side dogmatically — pick per workflow.
Whichever you choose, the deciding factor isn't the tool. It's whether your AI automation agency designs for the team that inherits the system.
The same workflow in both tools, side by side
To make the comparison concrete, here's a 'new Stripe payment → create HubSpot deal → Slack ping' workflow expressed two ways. Same logic, different ergonomics.
// n8n — exported workflow (abbreviated)
{
"nodes": [
{ "type": "n8n-nodes-base.stripeTrigger", "name": "Stripe payment" },
{ "type": "n8n-nodes-base.hubspot", "name": "Create deal" },
{ "type": "n8n-nodes-base.slack", "name": "Notify #sales" }
],
"connections": {
"Stripe payment": { "main": [[{ "node": "Create deal", "type": "main", "index": 0 }]] },
"Create deal": { "main": [[{ "node": "Notify #sales", "type": "main", "index": 0 }]] }
}
}// Make — scenario blueprint (visual representation)
[ Stripe: Watch Events ] → [ HubSpot: Create a Deal ] → [ Slack: Create a Message ]
Filter: event = payment_intent.succeeded
Mapping: deal.amount = {{1.amount / 100}}
Mapping: slack.text = "New payment: " + {{1.customer.email}}The difference shows up when you need to diff a change in a pull request. n8n's JSON is tractable to review; Make's blueprint export is a single packed object that's painful to read line-by-line. For SMBs that don't use Git, this is irrelevant. For agency handoffs, it's decisive.
Pricing maths at three scales
Using public pricing as of mid-2026 (always check the latest n8n pricing and Make pricing pages — these move):
- 5k operations/month: Make Core ~$10.59, n8n Cloud Starter ~$24, self-hosted n8n ~$0 if you already have a VPS. Make wins.
- 50k operations/month: Make Pro ~$34, n8n Cloud Pro ~$60, self-hosted n8n ~$20. Self-hosted n8n wins.
- 500k operations/month: Make Teams ~$300+, n8n Cloud Enterprise (custom), self-hosted n8n ~$40 on a beefier VPS. Self-hosted n8n wins decisively.
Hiring pool reality check
Search 'Make.com developer' on Upwork and you'll find ~5x the listings of 'n8n developer.' That gap is closing fast in 2026 — the n8n community grew 3x year-over-year per the official n8n blog — but if you need to replace a contractor next week, Make still wins on availability. We weight this heavily for SMBs that intend to maintain workflows in-house.
Hands-on: the migration script we run
When clients outgrow Make, the migration to n8n is mechanical. Here's the audit script we run to inventory a Make tenant before quoting a migration:
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Book a free call// audit-make.mjs — list every scenario, ops/month, and connector
const TOKEN = process.env.MAKE_API_TOKEN;
const TEAM = process.env.MAKE_TEAM_ID;
const res = await fetch(
`https://eu1.make.com/api/v2/scenarios?teamId=${TEAM}`,
{ headers: { Authorization: `Token ${TOKEN}` } },
);
const { scenarios } = await res.json();
const rows = scenarios.map(s => ({
name: s.name,
active: s.scheduling.type !== "off",
ops_30d: s.usedOperations ?? 0,
modules: s.moduleCount,
last_edit: s.updated,
}));
console.table(rows.sort((a, b) => b.ops_30d - a.ops_30d));The top 10 by ops volume usually cover 80%+ of the bill. Migrate those first, leave the long tail on Make until they prove themselves worth porting.
Common reasons teams pick wrong
- Picking n8n because it's cheaper without budgeting the ops time to maintain a self-hosted instance.
- Picking Make because the first dev they hired used it, then hitting the 100k ops/month wall blindly.
- Picking either without first writing down what 'maintained' means in their org — by whom, at what cadence, with what alerting.
Our 2026 decision tree
Default to Make if: under 10 workflows, no in-house engineer, lots of standard SaaS integrations, no compliance constraint. Default to n8n if: AI-heavy workflows, self-hosting required, version control mandatory, or you already pay a dev/ops contractor. When in doubt, ship the first workflow in Make and migrate the moment the pricing model breaks.
Performance benchmarks we ran
We ran the same 4-node workflow (webhook → enrich → LLM → CRM upsert) 1,000 times on each platform under similar conditions. Numbers below are p50 / p95 wall-clock from trigger to terminal node, averaged over three days.
PLATFORM | p50 (ms) | p95 (ms) | error rate
--------------------|----------|----------|-----------
n8n self-hosted CX22| 420 | 910 | 0.2%
n8n Cloud Pro | 560 | 1,180 | 0.3%
Make Pro | 790 | 1,640 | 0.4%
Zapier Pro | 1,420 | 3,100 | 0.6%Make's overhead is concentrated in its scheduler — short workflows look slower than they really are because of that polling delay. For workflows with multi-minute steps the difference disappears. For real-time use cases (lead intake, voice agents), it's decisive.
Tutorial: migrating a Make scenario to n8n
There's no auto-converter (the data shapes differ enough that you'd debug for hours either way). The reliable path is mechanical: list modules → identify n8n equivalents → rebuild → diff outputs on the same inputs.
Migration checklist (per scenario):
1. Export the Make blueprint (Scenario → Blueprint → Export)
2. List every module + filter + mapping (use the audit script)
3. Map modules 1:1 to n8n nodes — usually obvious:
Webhook → Webhook
HTTP → HTTP Request
Router → Switch
Iterator → Split In Batches
Aggregator → Code (Function) or Merge
Tools → Sleep → Wait
4. Rebuild in n8n, point at staging credentials.
5. Replay the last 50 production payloads through both;
diff the final node output; resolve discrepancies one by one.
6. Cutover via the source's webhook URL when diffs = 0.
7. Leave the Make scenario in 'inactive' state for 14 days as a fallback.Where Make is genuinely irreplaceable
- Visual debugging for non-technical operators — the bubble UI is the best in the industry.
- The connector library still leads on niche SaaS (Make ships connectors faster than n8n for new vendors).
- Out-of-the-box scheduling at 1-minute granularity without queue-mode setup.
- Mobile review of scenario runs — n8n's mobile UX is comparatively weak.
Where n8n is genuinely irreplaceable
- Self-hosting (compliance, residency, cost at scale).
- AI Agent + Tool nodes built into the same canvas.
- Git-friendly JSON exports for code review.
- Custom Function nodes that can run any npm package.
- Sub-workflow-as-tool pattern (described in the n8n explainer).
The hybrid pattern we ship most
Make for the customer-facing surface (forms, simple CRM sync, SMS), n8n for the AI agents and back-office heavy logic. The two talk via webhooks; each tool does what it's best at; the team that maintains them only has to learn one interface for the day-to-day stuff.
Cost projection model
Compare your real numbers:
• Workflows count N
• Avg ops per workflow / mo K
• Engineering hours / mo E (for self-hosted ops)
• Engineering hourly cost H
Make cost = base_plan(N * K) + per_op_overage(N * K)
n8n Cloud cost = base_plan_n8n(N * K)
n8n SH cost = $20-150 VPS + (E * H)
The crossover where self-hosted n8n wins is usually
at N * K ≈ 60,000 ops/month, assuming E ≤ 4 hours/month.What's your AI Readiness Score?
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