Compare n8n, Zapier and Make using operational requirements

A generic feature checklist cannot choose an automation platform for a specific process. Weight the requirements, verify current evidence and run the riskiest proof before committing.

Score anonymous candidates

The automation lab scores three authored candidates.

from automation_cases import platform_matrix_case

result = platform_matrix_case()
assert result["scores"] == {"platform-a": 25, "platform-b": 22, "platform-c": 19}
assert result["selected"] == "platform-a"
assert result["named_platform_benchmark"] is False
assert result["feature_verification_pending"] is True

Self-hosting receives the highest requirement weight, so platform-a wins. These candidates are not mapped to n8n, Zapier or Make and do not benchmark them.

Compare the process needs

Evaluate deployment/data boundary, identity and roles, approval pauses, connector actions, idempotency, error/replay semantics, queues, execution history, versioning/export, secrets, environments, API limits, support and owner skill. Model event volume and cost using current quotes rather than a static blog table.

Use official documentation dated at review time. n8n documents retrying failed executions; Zapier documents Human in the Loop states and tool approval; Make documents retry handlers and incomplete executions. Exact availability and plan limits can change, so verify them in the intended account and contract.

Prototype one failure-heavy flow: approval timeout, ambiguous external write, rate limit and replay. Record what the owner sees and how evidence is exported. Choose the platform whose verified operations meet the brief, not the one with the longest connector catalogue.

The Generative & Agentic AI course teaches platform-neutral process design and evidence-led selection.

Exercise

Build a weighted matrix for your workflow, cite every feature row and run a small proof for the top two. Keep unverified cells as pending rather than scoring assumptions.

Continue learning

This article is part of the Business automation with AI sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in No-code workflow retries: prevent duplicate side effects.
• Continue with Monitor a no-code automation for silent failures.

References: n8n executions, Zapier run statuses and Make incomplete executions.