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.
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.
2. Pricing & limits
| Feature | n8n (Cloud) | n8n (Self‑hosted) | Make (Cloud) | Free alternatives |
|---|---|---|---|---|
| Base price | Monthly plans tiered by executions (n8n pricing) | Free (open-source) – you pay for server resources | A free tier, then monthly plans tiered by usage (Make pricing) | Zapier and Power Automate both have free tiers with monthly task limits |
| Going over the limit | Move up a tier (check the pricing page for overage terms) | $0 per execution (only compute cost) | Buy extra usage or move up a tier (check the pricing page) | N/A |
| Billing unit | Executions (run of a workflow) | N/A | Usage per module run (see Make's pricing page for the current unit) | 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 fine for testing, but its monthly usage cap runs out quickly with AI calls.
- If you need predictable cost, n8n’s per‑execution pricing is simpler than Make’s per‑operation model.
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 |
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.
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. |
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. |
7. Free or cheaper alternatives
- Zapier Free – 100 tasks/mo, 5‑step Zaps, limited AI actions (OpenAI via Webhooks). Good for tiny automations.
- Microsoft Power Automate – has a free tier with full access to Microsoft connectors; AI Builder use is limited. Works well if you’re already in the Microsoft ecosystem.
- Automate.io (now part of Notion) – Free tier retired; not a viable option.
- Baserow + n8n self‑host – Pair a free open‑source database (Baserow) with n8n for a zero‑cost stack.
8. Implementation checklist
- Define KPI – executions per month, latency tolerance, data‑privacy level.
- Select deployment – cloud n8n vs Make vs self‑hosted n8n.
- Map integrations – list required APIs; verify they exist as native nodes.
- Prototype – build a single‑step workflow, measure execution time and cost.
- Add error handling – use Try‑Catch (n8n) or error routers (Make) early.
- Version control – export JSON (n8n) or snapshot (Make) to Git.
- Monitor – set up alerts on execution failures; both platforms provide webhook notifications.
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 is enough to prototype a few 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.
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.
On pricing: plans and rates change, and this post was written on a specific date. Check the vendor's current pricing page before you commit to anything.
FAQ
- Can I run n8n for free?
- Yes, n8n is open‑source; you only pay for the server it runs on. A small VPS is enough for most small-business workloads; compare current VPS prices before you pick one.
- What is the biggest limitation of Make’s free tier?
- It has a monthly usage cap, and AI calls use it up quickly. Check Make's pricing page for the current free-tier limit.
- Do I need programming skills to use n8n?
- Basic node configuration is visual, but custom logic (e.g., JavaScript functions) may require some coding.
- Is there a GDPR‑compliant way to use n8n?
- Self‑hosting n8n behind your own TLS and data store satisfies GDPR, provided you manage consent and data retention.
- Which tool has more native AI integrations?
- Make offers built‑in OpenAI, Azure OpenAI, and Google Vertex AI modules; n8n provides the same via HTTP nodes plus community‑maintained OpenAI and Anthropic nodes.
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