n8n vs Make: Which AI‑powered workflow automation fits a small business?

TL;DR

If you need a self‑hosted, low‑cost solution with full code access, go with n8n. If you prefer a polished cloud UI, extensive pre‑built integrations, and are willing to pay for higher task limits, Make (formerly Integromat) is the better fit. Both can be combined with free options like Zapier’s free tier or Microsoft Power Automate’s community plan for specific edge cases.

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1. Core premise

Both n8n and Make let you connect APIs, services, and databases without writing glue code. They support conditional logic, loops, and, increasingly, AI‑augmented actions (e.g., OpenAI text generation, embeddings, image analysis). The difference lies in deployment model, pricing granularity, and UI ergonomics.

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2. Pricing & limits (as of Aug 2026)

Feature	n8n (Cloud)	n8n (Self‑hosted)	Make (Cloud)	Free alternatives
Base price	$20/mo for 20,000 executions	Free (open‑source) – you pay for server resources	Free tier: 1,000 operations/mo, 100 MB data transfer; Paid: $9/mo for 10,000 operations, $29/mo for 100,000	Zapier Free: 100 tasks/mo; Power Automate Community: 750 runs/mo
Execution cost	$0.002 per additional execution	$0 per execution (only compute cost)	$0.001 per extra operation (pay‑as‑you‑go)	N/A
Billing unit	Executions (run of a workflow)	N/A	Operations (each node processed)	N/A
Self‑hosting	No	Docker, Kubernetes, or binary	No	No
SLA	99.9 % (cloud)	Depends on your infra	99.9 % (cloud)	N/A

Key takeaways

• n8n’s self‑hosted option is truly free aside from compute; ideal for developers comfortable with Docker or a small VPS.
• Make’s free tier is generous for testing but caps at 1,000 operations, which can be exhausted quickly with AI calls.
• If you need predictable cost, n8n’s per‑execution pricing is simpler than Make’s per‑operation model.

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3. Feature matrix

Category	n8n	Make
Deployment	Cloud SaaS or self‑hosted (Docker, Kubernetes, binary)	Cloud‑only (multi‑region)
AI nodes	Built‑in OpenAI, Anthropic, Cohere, Hugging Face; custom HTTP nodes for any model	Built‑in OpenAI, Azure OpenAI, Google Vertex AI; custom HTTP modules
Trigger types	Webhooks, cron, polling, event‑driven (e.g., GitHub, Stripe)	Webhooks, schedule, polling, built‑in app events
Number of integrations	~300 community‑maintained, plus any REST API via HTTP node	~1,200 native apps, plus HTTP module
Visual editor	Node‑based canvas, zoomable, keyboard shortcuts	Scenario builder with drag‑and‑drop, auto‑layout, real‑time data preview
Error handling	Try‑Catch nodes, manual retry, workflow‑level error triggers	Built‑in error routers, automatic retries, configurable back‑off
Version control	Export JSON, Git integration via CLI, workspace‑level versioning	Scenario versioning, snapshot restore, limited Git export
Extensibility	Custom JavaScript code, npm packages, custom nodes via Docker	Custom functions (JS), HTTP modules, limited external code execution
Security	OAuth2, API keys, JWT, self‑hosted TLS, role‑based access control (RBAC)	GDPR‑compliant, SOC 2, encrypted at rest, role‑based permissions
Support	Community forum, paid enterprise support, Slack channel	Email support for paid plans, community forum, extensive docs

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4. Deployment considerations

n8n self‑hosted

• Pros: Full control over data, no per‑execution ceiling, can run on a $5‑$10/month VPS. You can lock down network access, run behind a corporate proxy, and keep AI API keys off third‑party servers.
• Cons: Requires ops knowledge (Docker, updates, backups). Scaling beyond a single instance means managing a cluster or using the n8n Cloud tier.

Make (cloud only)

• Pros: No infrastructure to maintain, automatic scaling, built‑in UI for monitoring usage, instant access to premium connectors (e.g., Salesforce, HubSpot).
• Cons: All data traverses Make’s servers; compliance‑heavy businesses may need a DPA. Execution latency can be higher during peak load.

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5. Practical use‑case comparison

Use case	n8n implementation	Make implementation
Lead enrichment with AI	1️⃣ Webhook from Typeform → 2️⃣ HTTP node to OpenAI gpt‑4o for summarizing lead notes → 3️⃣ Update CRM via REST → 4️⃣ Store result in PostgreSQL. All steps run on a private VPS, keeping PII inside your network.	1️⃣ Typeform trigger → 2️⃣ OpenAI module → 3️⃣ HubSpot “Update contact” module → 4️⃣ Google Sheets logger. Works out‑of‑the‑box, but each node counts as an operation (≈4 ops per lead).
Invoice OCR + classification	1️⃣ Watch folder on S3 → 2️⃣ Call Azure Computer Vision OCR via HTTP → 3️⃣ Pass text to Cohere classify → 4️⃣ Insert into MySQL. Custom nodes let you batch 10 PDFs per execution, saving ops.	1️⃣ Dropbox watch → 2️⃣ Built‑in OCR module (Google Vision) → 3️⃣ Make’s AI module (OpenAI) → 4️⃣ QuickBooks connector. Simpler UI, but each PDF incurs 3‑4 operations, quickly hitting free limits.
Scheduled social‑media posting	Cron node → OpenAI dall‑e‑3 image generation → HTTP POST to Twitter API. Fully automated, cost limited to OpenAI usage only.	Scheduler → Make’s “Twitter” app → Content generated via OpenAI module. UI shows calendar view, useful for non‑technical team members.

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6. When to choose each tool

Situation	Choose n8n	Choose Make
Budget‑tight, dev‑savvy	You can host on a cheap VPS and need unlimited executions.	–
Strict data residency	Self‑hosted keeps data on your own servers.	–
Need for dozens of native SaaS connectors	Community nodes cover most, but you’ll write HTTP calls for niche apps.	Make’s library includes niche CRMs, marketing platforms, and legacy ERP systems.
Non‑technical team members	Canvas is clean but requires some node‑level understanding.	Scenario builder is more wizard‑like, with inline docs per module.
Heavy AI usage	You can call any model via HTTP, control token usage precisely.	AI modules are limited to OpenAI/Azure/Vertex; custom HTTP calls possible but less ergonomic.
Compliance requirements (SOC 2, GDPR)	You can certify your own instance, but you must manage audits.	Make already holds SOC 2 and GDPR certifications; easier for regulated firms.

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7. Free or cheaper alternatives

1. Zapier Free – 100 tasks/mo, 5‑step Zaps, limited AI actions (OpenAI via Webhooks). Good for tiny automations.
2. Microsoft Power Automate Community Plan – 750 runs/mo, full access to Microsoft connectors, AI Builder limited to 500 calls/mo. Works well if you’re already in the Microsoft ecosystem.
3. Automate.io (now part of Notion) – Free tier retired; not a viable option.
4. Baserow + n8n self‑host – Pair a free open‑source database (Baserow) with n8n for a zero‑cost stack.

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8. Implementation checklist

1. Define KPI – executions per month, latency tolerance, data‑privacy level.
2. Select deployment – cloud n8n vs Make vs self‑hosted n8n.
3. Map integrations – list required APIs; verify they exist as native nodes.
4. Prototype – build a single‑step workflow, measure execution time and cost.
5. Add error handling – use Try‑Catch (n8n) or error routers (Make) early.
6. Version control – export JSON (n8n) or snapshot (Make) to Git.
7. Monitor – set up alerts on execution failures; both platforms provide webhook notifications.

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9. Final recommendation for solo founders

• Start with Make if you need a quick proof‑of‑concept and don’t want to manage servers. The free tier covers up to 1,000 operations, enough for a few dozen AI‑enhanced automations.
• Switch to n8n self‑hosted once you hit the operation ceiling or need tighter control over API keys and data. The migration is straightforward: export the Make scenario as JSON, translate nodes to n8n equivalents, and import.
• Keep a fallback on Zapier’s free tier for one‑off tasks that require a non‑technical UI.

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10. Bottom line

Both platforms are mature, but they solve different problems. n8n gives you the freedom of open‑source, unlimited executions, and full code access at a minimal infrastructure cost. Make delivers a richer library of native connectors and a polished UI that reduces the learning curve for non‑developers. Choose the tool that matches your current constraints; you can always migrate later without rebuilding the entire logic.