Migrate Zapier to Make: A Practitioner's Migration Guide (2026)

I’ve migrated several client workflows from Zapier to Make.com over the last two years, mostly running Teedian content pipelines where the branching and iteration logic outgrew what Zapier could comfortably handle. The pattern is always the same: Zapier works fine until it doesn’t, and then it doesn’t in expensive, awkward ways.

If you’re reading “migrate zapier to make” as a query, you’ve probably already hit one of three walls: a task-based pricing bill that keeps climbing, a workflow that needs real branching or loops, or a debugging session where Zapier’s logs told you nothing useful. This guide walks the full migration: why it’s worth doing, when it isn’t, how concepts map between the two platforms, and a 6-step strategy that won’t break production.

Fair warning upfront: this is not a “switch everything tomorrow” post. Some Zaps should stay. Some shouldn’t exist at all. The decision needs to be per-workflow, not per-platform.

Why teams migrate off Zapier

Four reasons show up in almost every Zapier to Make.com migration I’ve done.

Pricing model mismatch. Zapier charges per task, where a task is roughly “one action ran”. Make charges per operation, where an operation is roughly “one module ran”. That sounds identical until you look at real workflows. A single Make scenario with a router, a filter, and an iterator can do the work of ten separate Zaps. You pay for the operations inside it, but the total operation count is usually 30 to 60 percent lower than the equivalent Zap task count, because Make lets you branch and transform without firing separate workflows.

Branching and iteration. Zapier still treats loops and conditional branches as second-class citizens. Paths exist but they’re rigid. Loops are possible with the Looping by Zapier app, but it’s a workaround that burns tasks. Make treats branching (Router) and iteration (Iterator + Aggregator) as first-class modules. If your workflow has more than one “if this then that” decision, Make’s model fits better.

Visual debugging. Make’s execution inspector shows every module’s input, output, and timing for every run. You click a bundle, you see the exact JSON that went in and came out. Zapier’s task history shows you stages, but drilling into what actually happened inside a step is slower and sometimes opaque. When you’re debugging a production workflow at 11pm, this matters.

Module depth. For common apps (Gmail, Slack, Google Sheets, Notion), both platforms are roughly equivalent. For complex APIs (HubSpot, Stripe, Airtable with advanced queries), Make tends to expose more granular modules. You get closer to the raw API without resorting to a webhook-and-HTTP workaround.

Data transformation. Make’s built-in function editor (string, math, date, array, regex) is richer than Zapier’s Formatter. Once you’ve written a few workflows that need real string manipulation or date math, Zapier’s Formatter starts feeling constrained.

When not to migrate

Migrating costs time. If none of these apply, save yourself the hours.

• You have two or three simple Zaps that work fine. The migration payback period is longer than the remaining life of those Zaps.
• Your team lives in the Zapier UI and retraining has a real cost. Make’s interface is more powerful but less forgiving, and non-technical operators will feel the learning curve.
• You depend on a Zapier-only integration. Check Make’s app list first. If a critical integration is Zapier-only and doesn’t have a public API you can call via HTTP, staying put is reasonable.
• You’re already evaluating self-hosting. In that case, skip Make and read Make.com vs n8n for production workloads. n8n self-hosted is a different cost and control model entirely.

If you want the full three-way comparison in German, I keep Make vs n8n im Vergleich updated.

Zapier concepts and their Make equivalents

Most of the mental model transfers directly. The vocabulary changes.

A few worth calling out. Filter in Make is not a module you drag in, it’s a property you set on the connection between two modules. Right-click the line between Module 3 and Module 4, choose “Set up a filter”, done. This feels weird at first and then becomes obvious.

Router replaces Zapier Paths. It takes one input bundle and sends it down any number of branches, each with its own filter. Unlike Zapier Paths, you can have a router inside a router, and you can have modules after the router that merge branches back together.

Data Store replaces both Zapier Tables and Storage. It’s schema-aware (you define fields and types), persistent, and queryable from any scenario in your org. There is no direct import from Zapier Tables, so plan a CSV export and re-upload.

The 6-step migration strategy

This is the sequence I use for any client migration. It’s boring on purpose. Boring is what production needs.

1. Inventory. List every active Zap. Group by criticality: production-critical (money moves, customer messages go out), operational (internal notifications, reports), and nice-to-have (cosmetic automations). Zapier’s dashboard lets you export this, or you can scrape it manually for small accounts. Tag each Zap with the integrations it touches. You’ll use this grouping in step 5.

2. Pilot. Pick one non-critical Zap. Rebuild it in Make. Run both in parallel for one to two weeks. The parallel run matters: it’s the only way to catch edge cases your test runs won’t cover (rare input shapes, rate limit behavior, timezone quirks).

3. Validate. Compare outputs side by side. Check error paths explicitly: trigger a deliberate failure, confirm Make’s error handler catches it, confirm the downstream system receives the same result Zapier would have produced. Check retry behavior (Make doesn’t auto-retry the way Zapier does, more on this below).

4. Cutover. Disable the Zap. Enable the Make scenario. Monitor for 48 hours. Keep the Zap in “off” state, not deleted, so you can roll back inside 30 seconds if something breaks.

5. Migrate in batches grouped by shared connections. If you have six Gmail Zaps, migrate all of them together so you authenticate the Gmail connection once and test the connection behavior once. Same for Slack, Notion, Stripe. Batching by shared connection is faster than batching by criticality.

6. Retire Zapier. Once nothing is active for a full billing cycle, cancel the subscription. Export task history first if you need the audit trail.

One thing I always do at step 2: write a one-page doc for each migrated workflow that describes the trigger, the outputs, the failure modes, and the rollback plan. Two months later when something weird happens, that doc is the difference between a 10-minute fix and a 2-hour archeology dig.

Translating common Zap patterns

Three patterns cover maybe 80 percent of Zapier workflows in the wild.

“New row in Sheets, send email”. In Zapier: Google Sheets trigger > Gmail action, maybe with a Formatter step in between. In Make: Google Sheets “Watch Rows” trigger > Gmail “Send an Email” action. The Formatter work moves inline into the Gmail module’s body field using Make’s functions, so there’s no intermediate module. Operation count: 2 per row, same as Zapier’s 2 tasks.

“Webhook, filter, Slack message”. In Zapier: Webhook catch > Filter > Slack post. In Make: Custom Webhook trigger > Filter (set on the connection) > Slack “Create a Message” action. The filter doesn’t consume an operation in Make. So three Zapier tasks become two Make operations.

“Schedule, API call, conditional, Notion update”. This is where Make pulls ahead. In Zapier you’d need a Schedule trigger, an HTTP action, Paths for the conditional, and separate branches each ending in a Notion action. In Make: Schedule trigger > HTTP request > Router with filters on each branch > Notion actions. Cleaner to read, cheaper to run, and you can merge branches afterward if you need a final logging step.

For AI workflows specifically (OpenAI, Anthropic calls), both platforms handle the basic “text in, text out” pattern fine. Once you need iteration over a list with AI calls on each item, Make is closer to n8n in flexibility. If you’re comparing LLM providers, Claude API vs OpenAI for business automation covers which fits which automation shape.

Cost math that actually works

Real numbers from the published pricing, 2026.

The translation isn’t 1:1. A Zapier task is one action step. A Make operation is one module run, and a scenario typically has three to six modules. So 1,000 Zaps per month, where each Zap averages one trigger plus two actions, is 1,000 task-equivalents on Zapier but roughly 3,000 to 4,000 operations on Make (including the trigger module).

Worked example. You run 1,000 workflows per month. On Zapier Professional that’s 1,000 of your 2,000 tasks, so $73/mo. On Make Core with 4,000 operations, you’re well inside the 10,000-op bucket, so $10.59/mo. As volume grows, the gap widens, because Make’s per-op pricing drops faster at scale than Zapier’s per-task pricing.

Where it flips: if your Make scenarios have 10+ modules each (heavy branching, iteration, lots of transformations), the operation count climbs fast. A 12-module scenario run 1,000 times per month is 12,000 ops, which pushes you into a higher Make tier. Still usually cheaper than Zapier at that volume, but not by the margin the simple math suggests.

The gotchas nobody warns you about

Things I wish someone had told me before the first migration.

Error handling is your job. Zapier silently retries failed tasks for you. Make does not. If an HTTP call fails with a 500, the scenario fails, the bundle is lost, and you get an email. You have to explicitly add an error handler route to every module that can fail, decide whether to retry, ignore, or route to a dead letter queue. This is covered in depth in Make.com vs n8n for production workloads. Don’t skip it.

Rate limits are your job too. Zapier throttles requests to external APIs based on plan limits and API-specific handling. Make runs as fast as the API allows, which means you can hit rate limits faster than you did on Zapier. For APIs with tight limits (HubSpot, Shopify admin), add explicit Sleep modules or configure the scenario’s max operations per interval.

Webhook URLs change. Every webhook trigger you migrate gets a brand new Make URL. You have to update every upstream system (Stripe, your own app, third-party services) that was hitting the old Zapier URL. Keep a spreadsheet of every webhook, its old URL, its new URL, and which upstream systems reference it. Test each upstream cutover individually.

Timezone quirks in scheduled triggers. Zapier and Make both let you set the timezone on schedules. They don’t always interpret DST the same way. Double-check every scheduled trigger after migration, especially ones that run at edge-of-day times (23:50, 00:10) where DST shifts can cause a job to run twice or skip a day.

Data Store migration. No import from Zapier Tables. Export to CSV, transform to match the schema you design in Make, import. For any table over a few hundred rows, write a small script rather than doing it by hand.

Stored authentication. Connections don’t transfer. Every OAuth connection (Google, Slack, Notion) has to be re-authenticated in Make. Do this in step 5 when you batch by connection, not piecemeal.

The first hour on Make

If you’ve never used Make, here’s the minimum to get calibrated.

1. Create a team (not just a personal workspace). Teams have proper role management even on the entry tier.
2. Build a Hello World scenario: Schedule trigger every hour, HTTP Make a request to https://httpbin.org/get, Tools “Set variable” to parse one field from the response. Run it once manually. Open the execution inspector and click through every module’s input/output bundle.
3. Add a Router after the HTTP module with two branches and different filters. Watch which bundles flow down which route. This is the mental model for everything else.
4. Create a Data Store with three fields. Add a module that writes one row. Query it with the “Search records” module in a second scenario. That’s 80 percent of the storage work you’ll ever do.
5. Check the scenario’s execution history view. That’s where you’ll spend most debugging time. Bookmark it.

An hour of this before you touch any real migration work saves a lot of fumbling later.

When to skip Make and go straight to n8n

Make is the right answer for most teams leaving Zapier. It isn’t the right answer for all of them.

Go straight to n8n if any of the following is true:

• Self-hosting is mandatory. DSGVO constraints, air-gapped environments, or strict data residency rules that rule out SaaS processors. Make is SaaS only.
• You need custom code nodes as a first-class citizen. Make has the Tools module and you can call out to your own HTTP functions, but n8n’s Code node runs JavaScript or Python inline and fits code-first teams better.
• Long-running workflows with heavy branching and thousands of executions per day. n8n scales better when you self-host on your own infrastructure, because you control the resources.
• You want to version-control workflows in Git. n8n exports workflow JSON cleanly. Make has blueprint export but it’s less ergonomic for Git-based workflows.

For everything else, Make is the lower-friction path. The full comparison is in Make.com vs n8n for production workloads, and the migration equivalent for the n8n path is migrate Zapier to n8n.

Which should you choose?

Short version.

• Stay on Zapier if you have fewer than five simple workflows, your team is non-technical, and cost isn’t a pain point.
• Migrate to Make if you need branching, iteration, better debugging, and predictable cost at volume. This covers most teams past the hobby stage.
• Go straight to n8n if you need self-hosting, custom code, or heavy scale on your own infrastructure.

Whatever you pick, run the 6-step strategy. Parallel run, validate, cutover, batch, retire. Skipping the parallel-run step is the single most common way migrations break production, and the one that costs the most to recover from.

Related reading

• Make.com vs n8n for production workloads
• Migrate Zapier to n8n
• Claude API vs OpenAI for business automation
• Make vs n8n im Vergleich (DE)