n8n vs Make: Picking the Right AI Workflow Builder

Both tools are popular for a reason, and both will disappoint you for a different reason once AI nodes enter the picture. This post cuts through the noise: how the two platforms actually differ, where each one breaks down, and what "AI-ready" really means when you're building something you'll rely on daily.

What You're Actually Choosing Between

Make (formerly Integromat) built its reputation on a visual drag-and-drop canvas that looks like a flowchart. Scenarios run in bubbles connected by lines. Non-technical users get comfortable with it quickly, and the template marketplace is large.

n8n starts from a different philosophy: your data, your logic, your code. Workflows are graphs of nodes. Every node is inspectable JSON. You can drop a Code node anywhere and write JavaScript or Python. Expressions use a template syntax ({{ $json.fieldName }}) that feels closer to a spreadsheet formula than a proprietary DSL. n8n is open-source, which means you can read, fork, and extend anything.

The surface difference is UX polish. The deeper difference is ownership and extensibility.

Node Model and Branching

Feature	Make	n8n
Canvas type	Linear / module chain	Free-form graph
Branching	Routers with filters	IF, Switch, Merge nodes
Looping	Built-in iterator	Loop Over Items + Split In Batches
Custom code	No native JS/Python	Code node (JS or Python)
Error handling	Error handler route	Error Trigger node, Try/Catch pattern
Expressions	Basic formula editor	Full JS expression engine

Make's router module handles simple yes/no branches cleanly. But nested conditions — "if field A is set AND field B matches this regex OR a previous step returned status 404" — require stacking routers in ways that quickly become hard to read.

n8n's IF node evaluates a condition and splits execution into two branches. The Switch node handles multi-way forks in a single step. You can reconnect branches downstream with a Merge node. Complex business logic that would need four or five Make routers often fits in one n8n Switch + one Merge. The graph stays legible.

Operation Limits vs. Execution-Based Pricing

This is the biggest practical difference for anything that runs frequently.

Make charges by operations: each module execution in a scenario counts as one operation. A scenario with eight modules that runs 1,000 times consumes 8,000 operations. If your workflow polls an API every minute and has ten steps, you burn through a Make Core plan (10,000 ops/month) in about 17 hours.

n8n Cloud charges by workflow executions, not individual node runs. A ten-step workflow that runs 1,000 times is 1,000 executions. This is not just a pricing quirk — it changes how you design workflows. On Make, you often flatten logic to save operations. On n8n, you can add as many nodes as the problem needs without watching an operation counter.

Practical impact: any AI workflow where an LLM node runs inside a loop (summarizing 500 emails, enriching a CRM list, processing a feed) multiplies fast on Make. On n8n, a loop with an LLM call inside is still one execution per outer trigger.

AI Integration — Where Both Fall Short Out of the Box

Here is the part most comparison posts skip.

n8n ships with a first-class AI Agent node, Chat Trigger, LangChain-compatible tool nodes, vector store nodes, and direct integrations with OpenAI, Anthropic, Google Gemini, Mistral, and others. The visual graph maps well onto agent architectures: tool nodes branch off an agent node, memory nodes attach, sub-workflows become tools.

Make has an OpenAI module and a handful of AI connectors. They work, but the mental model is "AI as one more module in a sequence," not "AI as the orchestrator of a graph of tools." For straightforward tasks — summarize this text, classify this email — Make is fine. For anything resembling an autonomous agent loop, n8n's architecture fits better.

The problem both share: you still need your own API key. OpenAI key, Anthropic key, Gemini key — you paste them into credentials, you pay a separate bill, you watch the usage dashboard. A busy n8n workflow hitting an LLM for every item in a 5,000-row import is several dollars of OpenAI spend on top of your n8n Cloud subscription.

Make has the same gap. The AI module works; the bill arrives separately.

What You Actually Own

This matters more than most people realize before they've built something meaningful.

With Make, your scenarios live in Make's cloud. There is an export format, but it is Make-specific. If Make raises prices, changes the operation model, or simply goes away, migration is painful.

With n8n, the workflow JSON is portable. n8n export:workflow --all gives you every workflow in a format the open-source runner can import. You can run n8n locally, on your own server, or anywhere — the workflows move with you. This is what "open-source" actually buys you in practice.

The self-hosting story for n8n is real. It is also genuinely work: Docker setup, Postgres configuration, SSL termination, keeping the image updated, managing environment variables for every credential. For a developer comfortable with infrastructure it is straightforward. For everyone else it is a project in itself.

Running Your Own n8n Instance Without the DevOps

If what appeals to you about n8n is the ownership model, the code node, the AI architecture, and the portability — but you do not want to run a server — that is exactly the gap AgentRoost was built to fill.

When you sign up on AgentRoost, you pick the n8n framework, name your instance, and your private n8n editor opens at https://<your-id>.agentroost.app within about two minutes. It is your instance: your login, your workflows, your data. Not a shared multi-tenant platform. Not a service where you file tickets to change settings. You get the n8n editor with full access — AI nodes already wired to included credits, webhooks on a public HTTPS URL with no tunneling, no Docker, no SSL certificate to provision.

The included AI credits are the meaningful difference from n8n Cloud or any self-hosted setup. On AgentRoost, the AI/LLM nodes work on day one with no API key. You pick from 350+ models and switch anytime. The credit usage is part of your subscription, not a separate bill. If you are building a workflow that calls an LLM on every item in a loop, that cost is absorbed — you are not watching an OpenAI dashboard nervously.

Plans start at $19.99/month, all-in. That covers the instance, the compute, and the AI credits. Monthly billing, cancel anytime, 14-day money-back guarantee.

Compare plans or see the n8n option.

Who Should Pick What

Choose Make if:

• Your team is non-technical and values the polish and template library
• Your workflows are linear with simple branching
• AI is a small part of the stack and you already have an OpenAI key you're managing elsewhere
• Operation counts work out for your volume (calculate honestly)

Choose n8n if:

• You need code nodes, complex branching, or agent-style loops
• You want workflow portability and open-source backing
• You are comfortable with self-hosting, OR you want your own instance without the DevOps overhead

Choose AgentRoost's n8n if:

• You want n8n's ownership model without running a server
• AI nodes need to work immediately, included in the price, no BYOK
• You want a public webhook URL and SSL handled for you from minute one

Quick Config Reference

For readers building their first AI workflow on n8n, here are the nodes you will use most:

Schedule Trigger      — cron expression, e.g. "0 8 * * 1-5" (weekdays 8am)
HTTP Request          — call any external API; set Authentication, Body, Headers
AI Agent             — the orchestrator; attach tool nodes as sub-nodes
LLM node (OpenAI…)   — model picker, system prompt, temperature
Set                  — shape the output object before the next node
IF                   — split on a condition (field equals, regex, number compare)
Code                 — drop to JS or Python for anything nodes cannot express
Merge                — rejoin two branches (Combine, Wait for Both modes)

A realistic "daily briefing" workflow: Schedule Trigger → HTTP Request (fetch RSS or API data) → Split In Batches → AI Agent (summarize each item) → Set (format output) → HTTP Request (post to Slack or email). Each AI call uses the included credits on AgentRoost; you never configure an API key.