Make.com vs n8n 2026: Cloud Platform vs Self-Hosted Automation
Make.com wins for no-code teams who want a polished cloud platform with zero server overhead. n8n wins for developers who need self-hosted control, code-level flexibility, and a path to zero recurring licensing cost.
Both are visual, node-based automation platforms. Both connect hundreds of apps. Both handle complex multi-branch workflows that Zapier's linear model cannot. But they diverge sharply on deployment model, pricing philosophy, and target user — and choosing the wrong one costs weeks of migration pain.
This guide cuts through the marketing and gives you the real decision criteria.
Quick comparison: Make.com vs n8n at a glance
| Make.com | n8n | |
|---|---|---|
| Deployment | Cloud-only (SaaS) | Self-hosted (Docker/npm) or cloud |
| Pricing model | Operations/month | Executions/month (cloud) or free (self-hosted) |
| Free tier | 1,000 ops/month | Free self-hosted; limited cloud trial |
| Paid cloud starting price | ~$10.59/month (Core) | ~$24/month (Starter) |
| Self-hosting | ❌ Not available | ✅ Docker, npm, or Kubernetes |
| Native integrations | 3,000+ apps | 400+ native nodes |
| AI capabilities | AI Agents, MCP server, OpenAI/Claude modules | LangChain nodes, AI Agent node, code execution |
| Workflow builder | Visual canvas (node-based) | Visual canvas (node-based) |
| Code execution | Limited (JSON/functions) | Full JavaScript/Python execution in-node |
| Learning curve | Low–Medium | Medium–High (developer-oriented) |
| Best for | Non-technical teams, agencies, rapid prototyping | Developers, DevOps, data engineers, self-hosting |
Deployment model: the biggest differentiator
Make.com: cloud-first, zero infrastructure
Make.com is a fully managed SaaS platform. You sign up, build scenarios in the browser, and Make handles servers, uptime, scaling, and security patches. There is no self-hosted option — your workflows and credentials live on Make's infrastructure.
This is the right model for teams that want automation without owning infrastructure. No Docker knowledge required. No VPS to maintain. No security patches at 2 AM.
The trade-off: Your data passes through Make's servers. For workflows handling sensitive PII, financial data, or regulated information, this may conflict with internal compliance requirements. Some enterprise plans offer data residency options in the EU or US, but the fundamental architecture remains managed cloud.
n8n: run it anywhere
n8n is open-source (fair-code license) and designed to be self-hosted. You can run it on a $6/month VPS, a Kubernetes cluster, an on-premise server, or n8n's own managed cloud. The source code is publicly available on GitHub.
Self-hosted deployment options:
- Docker:
docker run -it --rm --name n8n -p 5678:5678 n8nio/n8n— production-ready in under 5 minutes - npm:
npx n8nfor local development - Docker Compose: for persistent workflows with PostgreSQL and Redis backing
- Kubernetes: Helm chart available for large-scale deployments
The n8n Cloud option (hosted by n8n.io) exists for teams who want n8n's flexibility without running their own server — it starts at $24/month for 2,500 workflow executions.
The trade-off: Self-hosting requires real operational effort. You own uptime, backups, SSL certificates, version upgrades, and security. If your VPS goes down at 3 AM, your workflows stop.
Pricing: operations vs executions vs free
Make.com pricing
Make charges per operation — each individual action in a scenario counts. Sending an email is 1 op. Fetching data from an API is 1 op. A scenario that does 5 actions on each record processes 5 ops per record.
| Plan | Monthly price | Operations/month |
|---|---|---|
| Free | $0 | 1,000 |
| Core | ~$10.59 | 10,000 |
| Pro | ~$18.82 | 10,000 + priority execution |
| Teams | ~$34.12 | 10,000 + team management |
| Enterprise | Custom | Custom |
Operations can be purchased in add-on bundles if you exceed your plan. The Core and Pro plans allow increasing operations by paying more — check Make.com pricing for current rates since they update periodically.
The operations model is nuanced. A workflow that processes 1,000 records with 6 steps per record consumes 6,000 operations. Teams coming from Zapier's task-based model should audit their workflows before assuming Make will be cheaper.
n8n pricing
n8n's pricing model depends entirely on whether you self-host or use their cloud.
Self-hosted (free for personal/internal use): n8n is free to self-host under its fair-code license for personal projects and internal use. Companies using n8n for revenue-generating workflows or offering n8n-based services to clients need an Enterprise license — contact n8n for commercial pricing.
n8n Cloud:
| Plan | Monthly price | Workflow executions/month |
|---|---|---|
| Starter | ~$24 | 2,500 |
| Pro | ~$60 | 10,000 |
| Enterprise | Custom | Custom |
See n8n pricing for current rates.
The real cost of self-hosting n8n: a Liquid Web VPS running n8n comfortably starts around $15–$25/month (2GB RAM is sufficient for most teams). Add in 3–4 hours/month of maintenance time, and self-hosting costs a developer-hours premium but no per-operation billing — making it far cheaper than any cloud plan at scale.
Workflow builder: visual UX comparison
Both platforms use a visual node-based canvas. Drag a node onto the canvas, connect it to the next node with a line, configure inputs and outputs. The metaphor is similar; the execution is different.
Make.com: polished and intuitive
Make's scenario builder is widely considered the more beginner-friendly of the two. The canvas is clean, nodes are well-organized in a searchable library, and the data mapping interface (clicking fields to map inputs to outputs) is genuinely smooth for non-technical users.
Key UX advantages:
- Routers: fork workflow paths based on conditions with a visual branching interface
- Iterators and aggregators: process arrays one-by-one and collect results back into a single bundle
- Error handling: dedicated error routes with retry policies
- Scheduling: cron-style scheduling with a UI picker
Where Make shows its seams: deeply nested conditional logic and multi-branch workflows become visually complex fast. The scenario canvas doesn't scale elegantly to 50+ nodes.
n8n: power over polish
n8n's editor is functional but less refined. The node library is powerful, but the interface prioritizes control over simplicity. Non-technical users will hit friction faster; experienced developers will hit ceilings less often.
Key power features:
- Code node: run arbitrary JavaScript or Python directly in-node, with access to all upstream data — no need to leave the canvas for complex transformations
- Sub-workflows: call workflows from other workflows for reusable logic
- Error workflows: dedicated workflows that trigger on failure, enabling complex alerting and recovery logic
- Expressions: n8n's expression language exposes all upstream node data, not just mapped fields
For data engineers doing multi-step data transformations, the Code node eliminates the need for a custom integration — you can write the logic yourself.
AI capabilities: Make AI Agents vs n8n AI nodes
AI automation is where both platforms have shipped significant new capabilities through early 2026.
Make.com AI features
Make has launched several AI-native capabilities:
- AI Agents module: build goal-driven agents that chain tool calls (web search, database lookup, API calls) to complete multi-step tasks — configured without code
- MCP server integration: Make's Model Context Protocol server exposes Make scenarios as tools callable by AI assistants like Claude Desktop — enabling your AI assistant to trigger any Make scenario
- Maia AI assistant: an in-product AI that suggests scenarios and helps debug errors
- Native OpenAI and Anthropic modules: pre-built nodes for GPT-4o, Claude, and image generation
For non-developers building AI-powered workflows, Make's visual AI Agent builder is genuinely accessible. Describing a goal in natural language and having the platform wire up tool calls is a real capability.
n8n AI features
n8n's AI capabilities are deeper for developers:
- AI Agent node: orchestrates LangChain-based agents with memory, tool calling, and reasoning chains
- LangChain integration: native nodes for chains, memory, vector stores, embeddings, and retrievers — without writing Python
- Code node + LLM APIs: call any AI API (OpenAI, Anthropic, local Ollama models) with raw HTTP requests and process responses with JavaScript
- Vector store nodes: connect to Pinecone, Qdrant, Chroma, and others for RAG pipelines directly in the workflow
For teams building RAG pipelines, AI agents with custom tools, or LLM-powered data transformation, n8n's LangChain layer provides genuine depth. When you need something LangChain supports that n8n doesn't have a dedicated node for, the Code node covers it.
Integrations: 3,000 vs 400
Make advertises 3,000+ app integrations. n8n's native node library covers around 400+ apps. On paper, Make wins. In practice, the gap is smaller.
Why the gap narrows for technical users:
n8n's HTTP Request node connects to any REST API — no native node required. Any service with a documented API is reachable. Combined with the Code node, n8n handles virtually any integration that an experienced developer could build manually.
Why the gap matters for non-technical users:
For a marketing ops team that needs to connect HubSpot, Notion, Slack, Google Sheets, and a handful of SaaS tools without writing code, Make's 3,000+ pre-built integrations mean fewer friction points. If the app is in Make's library, setup takes minutes. With n8n, connecting a niche SaaS tool with an unusual API may require reading their docs and hand-crafting HTTP requests.
Make's integrations also tend to be more polished — trigger types, dynamic field dropdowns, and error handling are better-developed in Make's managed library than in some of n8n's community-contributed nodes.
Community and support
Make.com
- Paid plans include email/chat support; Enterprise plans include dedicated support
- Make Community forum is active and well-moderated
- Extensive official documentation with video tutorials
- Make Academy (free learning platform with structured courses)
n8n
- Large open-source community on GitHub (see the n8n repository for current star count) and a Discord with tens of thousands of members
- Community forum at community.n8n.io
- Self-hosted deployments rely heavily on community support; paid cloud plans include support SLAs
- Documentation is solid but assumes more technical knowledge than Make's
For self-hosted deployments, Google and the n8n community forum are your primary support channels. For teams running production workflows on self-hosted n8n, budget time for debugging infrastructure issues without a support contract.
When to choose Make.com
Make.com is the better choice when:
- Your team is non-technical: No Docker, no servers, no code. A marketing manager or ops lead can build and maintain workflows without engineering support.
- You need 3,000+ integrations out of the box: If your stack includes niche SaaS tools that need pre-built modules, Make's library depth matters.
- You want polished AI Agents without code: Make's visual AI Agent builder is the most accessible no-code option for goal-driven automation.
- Compliance allows managed cloud: If your data handling requirements permit SaaS storage, Make's infrastructure is secure and SOC 2 compliant.
- You value stability and support contracts: Paid plans include formal support; Make handles uptime for you.
Start building with Make.com →
When to choose n8n
n8n is the better choice when:
- You need self-hosting: Regulatory requirements, data residency rules, or internal security policy mandate that automation infrastructure stays on your own servers.
- You're a developer or data engineer: The Code node, Sub-workflows, and raw JSON control make n8n significantly more powerful for complex data transformation.
- You want to minimize recurring cost at scale: A self-hosted n8n instance on a VPS runs your entire automation stack for the price of a server. Operations billing disappears.
- You're building AI/LLM pipelines: n8n's LangChain integration and RAG pipeline support exceed what Make offers for technical AI workflows.
- You need code-level debugging: n8n's execution log exposes full JSON payloads at each node — essential for debugging complex data transformations.
Self-hosting n8n on Liquid Web: recommended setup
If you choose n8n for self-hosting, infrastructure choice matters. For most teams, a Liquid Web VPS provides the right combination of managed infrastructure quality and pricing.
Why Liquid Web for n8n hosting
- Managed VPS options: Liquid Web's managed VPS plans include 24/7/365 Heroic Support — someone to call when your n8n instance goes down at midnight
- Performance: SSD-backed VPS with guaranteed resources (no noisy-neighbor CPU throttling)
- Security: automated backups, server hardening, and optional DDoS protection
- Compliance: HIPAA-compliant hosting options for regulated workflows
Basic n8n Docker setup on a Liquid Web VPS
# 1. Install Docker (Ubuntu 22.04)
sudo apt update && sudo apt install -y docker.io docker-compose
sudo systemctl enable docker && sudo systemctl start docker
# 2. Create n8n data directory
mkdir -p ~/n8n-data
# 3. Run n8n with persistent storage
docker run -d \
--name n8n \
--restart unless-stopped \
-p 5678:5678 \
-v ~/n8n-data:/home/node/.n8n \
-e N8N_BASIC_AUTH_ACTIVE=true \
-e N8N_BASIC_AUTH_USER=admin \
-e N8N_BASIC_AUTH_PASSWORD=your_secure_password \
n8nio/n8n
# 4. Verify it's running
docker ps
After this, n8n is accessible at http://your-server-ip:5678. For production, add an Nginx reverse proxy with a Let's Encrypt SSL certificate.
Recommended Liquid Web plan for n8n: 2 vCPU / 4GB RAM handles 95% of small-to-medium teams. For workflows processing high data volumes or running many concurrent executions, 4 vCPU / 8GB RAM provides headroom.
Make.com vs n8n: side-by-side decision guide
| Use case | Winner | Why |
|---|---|---|
| Non-technical team building automations | Make.com | No infrastructure knowledge required |
| Developer needing custom data transformation | n8n | Code node + full JS/Python execution |
| Reducing per-operation billing at scale | n8n | Self-hosted = no operations billing |
| Connecting 20+ SaaS apps quickly | Make.com | 3,000+ pre-built integrations |
| Building LangChain / RAG AI pipelines | n8n | Native LangChain nodes |
| Building no-code AI agents | Make.com | Visual AI Agent builder |
| Self-hosting for data compliance | n8n | Full self-hosted deployment |
| Fastest time-to-first-workflow | Make.com | Lower learning curve |
| Complex multi-step data pipelines | n8n | Better error handling + Code node |
| Teams needing managed support SLA | Make.com | Enterprise support contracts |
FAQ
Is n8n better than Make.com?
Neither platform is objectively better — it depends on your requirements. n8n is better for developers, self-hosted deployments, and complex data transformations. Make.com is better for non-technical teams, rapid no-code prototyping, and workflows that need 3,000+ pre-built integrations. If data privacy or cost at scale are concerns, n8n's self-hosting model wins clearly.
Can I self-host Make.com?
No. Make.com is a cloud-only SaaS platform with no self-hosted option. All workflows and credentials are stored on Make's infrastructure. If self-hosting is a requirement, n8n is the right choice.
Which is cheaper: n8n or Make.com?
It depends on your usage model. For small-scale automations, Make.com's free tier (1,000 ops/month) is cheaper than n8n Cloud's paid plans. At scale, self-hosted n8n is significantly cheaper — your cost is the VPS (roughly $15–$25/month on Liquid Web) with no per-operation billing. n8n Cloud starts at ~$24/month for 2,500 executions, while Make.com Core starts at ~$10.59/month for 10,000 operations — though Make charges per action within a workflow, so those 10,000 ops often disappear faster than expected.
Does n8n have as many integrations as Make.com?
No. n8n has 400+ native integrations versus Make.com's 3,000+. However, n8n's HTTP Request node connects to any REST API, so technical users can reach virtually any service. The integration gap matters most for non-technical users who need point-and-click setup for niche SaaS tools.
Can n8n handle the same AI workflows as Make.com?
Yes, and often more powerfully for technical teams. n8n has native LangChain integration for building RAG pipelines, AI agents, and vector store workflows. Make.com's AI Agent module is more accessible for non-developers. For production LLM pipelines with custom memory, tools, and vector retrieval, n8n's depth is greater.