n8n vs Make: the real differences show up after your 50th workflow

Industry & Strategy · By Caleb Sakala · May 6, 2026

Every n8n vs Make article starts with a feature table. Integrations count, pricing tiers, UI screenshots. By paragraph three you know Make has 3,000+ integrations and n8n lets you self-host. That information stopped being useful about two years ago.

The comparison that matters happens three months after you pick a platform, when 40 or 50 workflows are running and something breaks on a Saturday morning. The architectural decisions behind each tool start to matter at that point, and feature tables won't prepare you for them.

n8n vs Make pricing: operations versus executions, and why both numbers mislead you

Make charges per operation. An operation is a single action within a scenario: one HTTP request, one filter, one data transform. A five-step workflow that processes 100 records consumes 500 operations. At Make's Pro tier ($10.59/month for 10,000 operations), that one workflow eats 5% of your monthly quota in a single run.

n8n charges per execution on its cloud tier. An execution is a full workflow run, regardless of how many nodes it touches. That same five-step, 100-record workflow counts as one execution.

Here's where both numbers mislead. n8n's self-hosted Community Edition has no execution limits, but hosting costs real money. A t3.medium EC2 instance on AWS runs about $30/month. Add a managed Postgres instance for workflow data and you're at $45-60/month. For teams running fewer than 20,000 Make operations monthly, Make's $10.59 Pro plan costs less than self-hosting n8n.

Cost comparison at different operation scales (May 2026 pricing):

10,000 ops/month:
  Make Pro:       $10.59
  n8n Cloud:      $24/mo (Starter, 2,500 executions)
  n8n Self-host:  ~$50/mo (AWS t3.medium + RDS)

50,000 ops/month:
  Make Teams:     $18.59
  n8n Cloud:      $60/mo (Pro, 10,000 executions)
  n8n Self-host:  ~$50/mo (same server handles it)

200,000 ops/month:
  Make Enterprise: custom pricing
  n8n Cloud:       $60/mo (still Pro tier)
  n8n Self-host:   ~$50/mo (vertical scaling may be needed)

The crossover point sits around 30,000-40,000 operations per month. Below that, Make costs less and requires zero infrastructure work. Above it, n8n self-hosted saves money every month that number grows.

Error handling is where the architectures diverge

Make's error handling works through a module-level system. You attach error handlers to individual modules, set up break/resume logic, and route failures to dedicated paths. The visual interface makes simple error cases easy.

The problem surfaces when errors cascade. A Make scenario that calls an API, transforms the result, and writes to a database has three potential failure points. If the API returns a 500, the error handler retries or routes to an alert. But if the API returns a 200 with malformed data that passes through the transform module and then crashes the database write, the error handler on the database module has no context about what the API originally returned. Each module operates in relative isolation.

n8n handles this differently. Because workflows are code-adjacent, you can insert validation nodes that carry context forward:

// n8n Function node: validate API response before it hits the DB
const response = $input.all();
for (const item of response) {
  if (!item.json.id || typeof item.json.amount !== 'number') {
    throw new Error(
      `Malformed record from API: ${JSON.stringify(item.json).slice(0, 200)}`
    );
  }
}
return response;

That validation sits between the API call and the database write. When it throws, the error contains the malformed payload, not just "database write failed." n8n's error workflow feature can then catch that context and forward it to Slack with the specific record that broke.

Make can approximate this with a router module and custom filters, but the logic lives in the visual builder. Past four or five conditions, the scenario view becomes a tangle of arrows that nobody on the team wants to debug.

The AI workflow gap neither tool has closed

n8n added an AI Agent node in late 2024 and has expanded it since. You can build agent-style workflows where an LLM decides which tools to call, with built-in memory support and vector store integration. Make added AI modules too, including ChatGPT and Claude modules, but the implementation treats LLM calls as standard HTTP-style modules without the loop-and-decide pattern that agent workflows need.

For a concrete example: building a workflow that reads a support ticket, classifies it, pulls relevant documentation from a vector store, drafts a response, and routes it for human review requires five nodes in n8n with the AI Agent node handling the classify-retrieve-draft sequence internally. In Make, the same workflow needs eight or nine modules with explicit routing between each step, because Make has no equivalent to n8n's agent loop.

# n8n AI Agent workflow (simplified node structure):
trigger: webhook (incoming support ticket)
nodes:
  - ai_agent:
      model: gpt-4o
      tools:
        - vector_store_search (documentation)
        - classify_ticket (custom function)
      system_prompt: "Classify, retrieve docs, draft response"
  - if_confidence_above_threshold:
      condition: "{{ $json.confidence > 0.85 }}"
  - send_to_review_queue  # high confidence
  - send_to_human_agent   # low confidence

That said, n8n's AI Agent node has its own constraint. When the agent decides to call a tool and the tool fails, the retry logic falls back to the same LLM-decides-next-step pattern with no structured escalation. LangChain's ReAct executor has the same blind spot: the agent retries the same failing tool call because nothing in the loop tells it to try a different path. Chase Agents routes this differently, where each step declares typed inputs and the platform validates data at step boundaries. When a tool call returns a schema mismatch, the step fails at the boundary rather than letting the agent loop on a broken tool three more times. That distinction prevents the "agent burned 40 API calls retrying a 404" problem that n8n and LangChain users report on GitHub.

An n8n vs Make decision framework that actually holds up

Pick Make if all four of these are true: your workflows have fewer than 10 steps, you run under 30,000 operations per month, nobody on the team can manage a Linux server, and you need connectors to niche SaaS apps that only Make supports.

Pick n8n if any of these are true: you need to run JavaScript or Python inside workflows, you process data at volumes where per-operation pricing becomes painful, you need to self-host for compliance, or your workflows involve multi-step error handling where context needs to propagate across nodes.

Pick neither if the automation problem is complex enough that building the workflow manually takes longer than describing what you want it to do. When the specification is simpler than the implementation, the build-it-yourself model (drag-and-drop or code-first) starts working against you. Spending four hours wiring up a ten-step procurement workflow with validation, error handling, and conditional routing is four hours that a natural-language-to-automation platform like Chase Agents compresses into a conversation. Whether that trade-off matters depends on whether building automations is your job or a distraction from it.

So which one wins?

Neither. n8n gives you control. Make gives you speed. The better question is whether you should be building the plumbing at all, or describing what the plumbing should do and letting something else assemble it.