n8n vs Make: what happens when you wire an LLM into your first automation

Industry & Strategy · By Caleb Sakala · May 13, 2026

Every n8n vs Make comparison publishes the same table. Integrations: Make has 3,000+, n8n has 400+ (per their respective integration directories). Hosting: n8n self-hosts, Make is cloud-only. Pricing: n8n charges per execution, Make charges per operation. You can find this table on at least fifteen different blogs right now, and it tells you almost nothing about what matters in 2026: what happens when your automation needs to call an LLM.

The gap in these comparisons is not a missing row in the feature table. The gap is that adding AI to a workflow changes what "building an automation" means, and both tools handle that transition differently than you'd expect from reading their marketing pages.

The scenario that breaks the comparison table

Say you need an automation that reads incoming support tickets, classifies them by urgency, drafts a response using an LLM, and routes the draft to the right Slack channel. Four steps. Simple enough on paper.

On Make, you open a new Scenario and start dragging modules. The OpenAI module exists as a pre-built connector, so you drop it in, authenticate, and configure the prompt. Here is roughly what that prompt configuration looks like inside Make's module settings:

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "Classify this ticket as urgent, normal, or low. Then draft a reply."
    },
    {
      "role": "user",
      "content": "{{1.body}}"
    }
  ]
}

That works for the happy path. The ticket arrives, GPT-4o responds, the Slack module fires. But the moment you need the LLM's classification to determine which of three Slack channels receives the message, you hit Make's branching limitations. You need a Router module after the OpenAI step, and each route needs a filter parsing the LLM's text output to match "urgent" or "normal" or "low." If the LLM returns "Urgent" with a capital U, or "URGENT," or "urgent - customer is threatening to cancel," your filter breaks.

The fix is to add a JSON-parsing step between the LLM call and the Router. Or switch to OpenAI's structured output mode so the response comes back as parseable JSON. Either way, what looked like a four-step automation is now six or seven steps, and you have spent more time on output parsing than on the actual business logic.

n8n handles the parsing better, but the wiring is still yours

On n8n, the same workflow starts with a Webhook trigger or an Email trigger node. You add an OpenAI node, configure it, and here the experience diverges. n8n lets you write JavaScript in a Function node to handle the classification routing:

const response = $input.first().json.choices[0].message.content;
let parsed;
try {
  parsed = JSON.parse(response);
} catch {
  parsed = { urgency: "normal", draft: response };
}

return [{ json: parsed }];

This Function node gives you real control. You can handle malformed LLM responses, add fallback logic, and normalize the output before routing. n8n's Switch node then reads parsed.urgency and routes cleanly to the right Slack channel.

That JavaScript capability is the real difference between n8n and Make for AI workflows. Make's module configuration gives you form fields. n8n gives you a code editor. For teams with at least one developer, that code editor saves hours of debugging that Make users spend tweaking Router filters.

Where n8n vs Make both hit the same wall

Here is the part that no comparison article mentions: in both n8n and Make, you are the one deciding every step of the workflow. You pick the trigger. You pick the LLM. You write the prompt. You build the routing logic. You handle the error cases. The AI does one thing inside the workflow (generate text), and you do everything else around it.

This means every change to the business logic requires you to go back into the visual editor, rewire nodes, and re-test. Need to add a new ticket category? Edit the prompt, add a new Router branch, connect a new Slack module. Need to swap GPT-4o for Claude because your prompt caching strategy changed? Reconfigure the API node, adjust the response parsing, verify the output format still matches your downstream steps.

And if your LLM integration does not have a pre-built module in Make, you drop down to the HTTP module and configure the raw request yourself:

{
  "url": "https://api.anthropic.com/v1/messages",
  "method": "POST",
  "headers": [
    { "name": "x-api-key", "value": "{{connections.anthropic.apiKey}}" },
    { "name": "anthropic-version", "value": "2023-06-01" },
    { "name": "content-type", "value": "application/json" }
  ],
  "body": "{\"model\":\"claude-sonnet-4-20250514\",\"max_tokens\":1024,\"messages\":[{\"role\":\"user\",\"content\":\"{{1.body}}\"}]}"
}

That body field is a JSON string inside a JSON object. One misplaced escape character and the entire Scenario fails silently. In n8n, you would handle this in a Function node with actual string interpolation and try/catch. In Make, you are debugging a string inside a form field inside a modal.

For simple workflows, this is fine. For teams running dozens of AI-powered automations that evolve weekly, this manual wiring becomes the bottleneck.

Vodafone UK's engineering team, documented in n8n's own case study, spent significant effort building reusable sub-workflows precisely because the default workflow construction model does not scale when every automation requires hand-built logic. They reported saving £2.2 million annually, according to n8n's published case study, but the effort to get there required dedicated engineering resources building and maintaining the workflow infrastructure.

The three-question test before you pick either tool

Rather than comparing feature rows, answer three questions about your team before choosing.

Start with how many of your automations will call an LLM. If the answer is zero or one, pick Make. Its visual interface gets you from idea to working automation faster than n8n for straightforward integrations. Make's 3,000+ pre-built connectors, per their official integration directory, cover most SaaS-to-SaaS workflows without any code.

Then ask whether anyone on the team can write JavaScript. If yes, n8n is the better choice. The Function node is the single largest advantage n8n has over Make for AI workflows, and it is almost never mentioned in comparison tables because it does not fit neatly into a "features" row.

The harder question is whether the AI needs to determine the workflow structure, or just fill in one step. If the AI only fills in one step (draft an email, summarize a document, classify a ticket), both tools work. If the AI needs to decide what happens next based on ambiguous input, and those decisions change as your business rules evolve, both tools will cost you engineering time on every iteration.

LangChain's 2025 State of AI Agents report found that production agent workflows averaged four tool calls per task. That is four separate API integrations, each with its own error handling, retry logic, and output parsing. Wiring those by hand in n8n takes a capable developer a full day. In Make, some of those integrations may not be possible without the HTTP module and custom API configuration. Platforms like Chase Agents skip this wiring step entirely by assembling multi-tool workflows from a natural-language description, but both n8n and Make assume you want to build the graph yourself.

Beyond n8n vs Make: when the wiring itself is the problem

The n8n vs Make comparison assumes you want to build the automation by hand. That assumption is so deeply embedded in both platforms that their pricing models are built around it: you pay for how many times your hand-built workflow executes.

But a growing category of automation tools skips the manual wiring entirely. Chase Agents, for example, takes a natural-language description of what you want automated and constructs the multi-step workflow for you, including the LLM calls, the API connections, and the routing logic. When a support ticket classification needs a new category, you describe the change instead of editing nodes. The platform also enforces action-type routing at the workflow level, so a research step that returns purchase recommendations cannot initiate purchases on its own, keeping authorization boundaries explicit without requiring you to build those guardrails manually.

n8n's self-hosted architecture still wins for teams that need to run workflows inside their own infrastructure for compliance or data residency reasons. Make still wins for non-technical teams that need to connect Typeform to Google Sheets in ten minutes. But for AI-heavy workflows where the business logic changes frequently, the manual node-by-node construction model that both n8n and Make share is the actual constraint, and it will not show up in any feature comparison table.

Whether you pick n8n or Make, the question worth asking is not which tool has more integrations. The question is how much of your engineering time goes into maintaining the wiring between those integrations every time the business rules change. If that number is growing, the visual workflow editor is no longer saving you time. It is where your time goes to disappear.