Zapier Make Patterns

---
name: zapier-make-patterns
description: Design reliable automations on Zapier and Make: trigger/action patterns, error handling, filters, and scaling. Use when building no-code integrations or choosing between the two platforms.
category: workflow-automation
---

# Zapier/Make Patterns

## Overview

Zapier and Make are the mainstream no-code automation platforms: thousands of app integrations, visual builders, managed infrastructure.

Zapier: simplest, most integrations, linear Zaps. Make: visual scenario builder, more flexible data flow, usually cheaper at scale.

Same reliability principles apply: idempotency, error handling, dedup, monitoring — the platform doesn't excuse the discipline.

## When to use

- Connecting SaaS apps without code
- Choosing between Zapier and Make for a use case
- Fixing flaky or failing Zaps/scenarios
- Scaling automation beyond a handful of flows
- Team automation with non-technical builders

## Core concepts

- **Trigger choice.**
  Instant (webhook) vs. polling triggers. Instant for responsiveness; polling intervals cost tasks/operations. Know which each integration offers.
- **Filters and paths.**
  Filter steps stop irrelevant runs early (saves tasks). Paths/routers branch logic. Filter first, branch second.
- **Multi-step with data mapping.**
  Map fields explicitly; test with real sample data. Data shape changes break mappings silently — verify after source updates.
- **Error handling.**
  Zapier: error notifications, replay, Paths for fallbacks. Make: error handlers, retries, break/ignore directives. Design the failure path, not just the happy path.
- **Dedup and idempotency.**
  Use dedupe keys, 'find or create' patterns, and storage (Tables/Data stores) to prevent duplicate side effects on re-runs.
- **Task/operation economy.**
  Every step costs. Combine steps, filter early, batch where possible. Audit monthly: which Zaps burn the most tasks?
- **Naming and folders.**
  Descriptive names (`[Sales] New lead -> CRM + Slack`), folders by team/process. Future you maintains what present you names well.
- **Testing discipline.**
  Test with real data, test the error path, test edits before publishing. Published-but-untested is production roulette.

## Practical workflow

1. **Map the flow.**
   Trigger, filters, steps, error path — on paper first. Especially the dedup strategy: what makes a run unique?
2. **Pick the platform.**
   Simple linear + max integrations -> Zapier. Complex branching/data ops + cost sensitivity -> Make. Prototype the hard part first.
3. **Build with filters first.**
   Filter steps immediately after trigger. Most wasted tasks come from unfiltered triggers firing on noise.
4. **Map data carefully.**
   Explicit field mapping with real sample data. Document assumptions about source data shape.
5. **Add error handling.**
   Notifications to an owner channel, replay/fallback logic, dead-letter pattern for unprocessable items.
6. **Test thoroughly.**
   Happy path, edge cases, error path. Break it deliberately to verify the error handling works.
7. **Name and organize.**
   Convention-based names, folders by domain, descriptions on complex flows. Handoff-ready.
8. **Monitor and prune.**
   Monthly: task usage per flow, error rates, still-needed check. Kill or fix the noisy ones.

## Common pitfalls

- **No filters on triggers.**
  Every event firing full multi-step flows. Filters first — the cheapest optimization in automation.
- **Ignoring error paths.**
  Default error behavior (stop, notify nobody useful). Design failures deliberately.
- **Task-burn blindness.**
  200-task Zaps running hourly on polling. Audit consumption; the bill is the feedback.
- **Brittle field mapping.**
  Mapped once, source app changes fields, flow silently misfires for weeks. Re-verify after any source change.
- **No dedup.**
  Re-runs creating duplicate CRM records, emails, invoices. 'Find or create' and dedupe keys from day one.
- **Spaghetti scenarios.**
  Make scenarios with 40 modules and crossing lines. Break into smaller scenarios; document the data flow.
- **Untested publishing.**
  Turning on flows tested with one perfect sample. Real data is messy — test with it.
- **Orphaned flows.**
  Builder leaves; flows keep running (and billing). Ownership registry; offboarding includes automation handoff.