Zapier vs Make for Remote Teams Who Automate Everything

1. Integration depth between Zapier and Make for core remote tools

The first thing I noticed—before I even got anything working—is how different the two platforms feel when connecting to the same tools. Slack, Google Sheets, Notion, Airtable… they all have official support on both Zapier and Make, but they’re absolutely not equivalent.

Take Slack: Zapier offers fewer event types out of the box. For instance, you can’t trigger on button clicks from interactive Slack messages without a workaround, and updating block templates dynamically is… not really a thing. In Make, though, their Slack module exposes a lot more config by default. You can dig into the thread ts, raw JSON message payloads, easily handle mentions—it’s just deeper.

One time, I needed to respond to a specific button click in a Slack message to launch a follow-up form. Zapier couldn’t see the action. Make not only picked up the interaction—using Slack’s Events API and interactivity settings—but it let me branch the flow depending on the exact button ID clicked.

Where Zapier shines is speed to integration. Need to send a Slack message when a Google Form is submitted? Takes 2 minutes. But Make gives you more control, especially when dealing with complex message formatting, inline buttons, or threading.

But here’s where it glitched: in Make, if you don’t set the block_id but reuse the same message ts in an update module, Slack throws back a “message_not_modified” error silently—and your automation just… stalls. No error, nothing visible.

Meanwhile, Zapier will fire off without checking if it’s the same message content—doesn’t matter if it actually changed. That can be helpful or annoying depending on the context.

2. Error handling differences when something goes completely sideways

This is where Make saved my entire Monday once. I had a flow pulling Zapier Webhook data into Airtable that broke when someone added a new field—one extra column for “Client Status” and the Zap just silently skipped it. No warning, no failure log, just… data missing.

Zapier only shows errors if a step hard-fails. If a column doesn’t match? It quietly moves on.

In Make, that same flow would have hit an explicit halt with a red X. And that’s actually what I like: Make lets you build catch branches (error paths) that trigger when things fail. It’s a little like writing try/catch blocks in your automation. Zapier finally rolled out “Paths” recently, but they’re more for logic branching than error recovery.

Make’s error handlers can pause and wait—which is crucial

• You can define what happens on failure—retry after 10 mins, notify via email, log to a row
• You can clone modules conditionally only on error branches
• The UI shows exactly where it failed, not just which step
• You can jump into the JSON payload directly and fix inputs manually for reruns
• Zapier’s interface trims logs after a short time—Make lets you store longer history

Undocumented edge case I hit in Zapier: if your webhook response is valid JSON but contains a top-level array instead of an object, some input mappers freak out silently. You don’t get an error; you just get no variables. Make handled the same payload with zero issues.

3. Complicated branching logic and dynamic data manipulation flows

Here’s the raw deal: conditional logic in Zapier is clean and readable, but painfully limited. If you need nested IF/ELSE complexity, multiple AND conditions, or switches based on mapped value types, you run out of native tools fast. Make is like—almost no guardrails. You can build deeply nested routers, use flow controls with iterator arrays and cycles—it’s overkill until one day you need it.

First time I ran into this was trying to auto-assign leads based on email domain, region, and seniority. Zapier couldn’t bundle those checks without ugly workarounds. In Make, I literally just added a router with six branches—each one with separate filters—and it worked on the first try.

And here comes the platform behavior bug: Zapier’s Paths don’t support dynamic lookup tables without multiple layers of Formatter/Lookup steps chained together. Even then, if your lookup returns null? Subsequent steps assume it’s a text value like “null”, not an actual empty.

But Make lets you use built-in functions like contains(), ifempty(), and even map() in expressions. That means you can apply conditional field formatting right inside data mapping screens, without resorting to multiple Formatter steps.

4. Managing webhooks and trigger setup across remote systems

Webhook hell hits different when you’re working async across teams in different time zones.

I can’t count how many times I’ve pinged someone to test a webhook form, only to realize Zapier had auto-disabled the webhook due to inactivity. There’s no warning until you try to test it—then you’re like “why isn’t this firing?” and after clicking around for 10 minutes, find the tiny red text saying the trigger’s been disabled.

Make doesn’t do this. Their webhooks stay live unless you delete them. In fact, every scenario in Make can be configured inside a webhook handler module—with multiple triggers per scenario. You can even inspect raw headers directly in the webhook module.

What finally convinced me: when a client sent a malformed Content-Type header (should’ve been application/json but came in as text), Zapier rejected it entirely. Make parsed it anyway. Didn’t crash, didn’t error. Just gave me a raw body buffer I could parse manually using parseJSON().

This made it way easier to debug third-party webhook integrations like form tools, custom PHP or Node.js apps teammates were deploying in weird environments (one was hosted on some Raspberry Pi setup—don’t ask).

5. Version control and visibility into flow history

There’s no undo in Zapier. That’s not an exaggeration—that’s literally how it works. You can’t see what changed, when, or why. If you tweak a Formatter step and forget what you removed? Good luck.

Make has minimal versioning, but it shows history per module run. That means you can replay an exact run, click any module, and see the inputs and outputs during that execution—including intermediate values. Even the expressions used. It’s not Git, but it’s way closer to actual flow debugging than Zapier allows.

The workaround I built in Zapier was to duplicate every Zap first, so I had a backup. Brutal, but necessary. Otherwise it’s one-way editing. Once, I changed an Airtable field from single-line to multi-select without realizing the impact—Zapier started sending arrays of arrays and broke the webhook listener silently. Make? Filter flagged it immediately as an incompatible input type.

Another subtle bug: Zapier doesn’t warn you when deleting a Formatter step that feeds into later steps. So those steps just revert to blank—or worse, retain stale values. In Make, deleting a module breaks every downstream module dependent on its output. That’s annoying, but at least obvious.

6. Scheduling and rate limiting handling when automating in distributed teams

Between me in California, my dev in Poland, and ops in Singapore, we fire flows at all hours. Make’s scheduling is minute-precise and still somehow feels gentler on rate limits. Probably because they use sequential execution unless you deliberately parallelize.

Zapier fires instant triggers with no stacking logic. So if three updates hit within a second, you get three Zaps—instant. Useful, but can induce throttling. I triggered our Monday.com API limit once just by importing one Google Sheet that stacked 200 updates all in a 5-second window.

Why Zapier’s delay steps aren’t panaceas:

Zapier offers delay by minutes, but not real scheduling granularity. You end up crafting weird Formatter steps like “if hour equals 2, continue” to simulate time-based logic. Make lets you schedule flows for weekdays only, run every 10 minutes from 9-5, or cycle every few seconds if needed.

And you can throttle loops in Make with built-in iterators. Zapier’s looping is an early beta feature and resets variables at weird points. One time, iterating over a JSON array with three records? Zapier fired three runs, not one multi-item loop. Still unresolved.

7. Cost implications based on execution count and team activity

I didn’t budget for it, but tap a shared Zap 200 times accidentally because of a looping filter mistake, and that’s $20 gone just like that.

Zapier charges per Zap run regardless of outcome. Even test runs. Even failures. Make charges per operation (step in a scenario), so technically more granular—but it gives you more control too. If you build efficiently, especially by bundling steps using iterator modules, you can process 50 rows in fewer operations.

“Why did Make say I used 842 operations?” is usually someone forgetting to filter an iterator and accidentally parsing 100 Slack channels into 20 AI summarizer calls.

But at least you can see it. Zapier hides usage behind a UI that’s constantly reminding you of upgrades, but shows no detailed run pattern breakdown. Make shows scenario cost per run and even flags which modules consume the most.

If you’re in remote work, where asynchronous work floods your data pipelines at odd hours, Make’s cost model makes more sense. Pay for what breaks. Zapier, it’s: pay for what happened, whether or not it was useful.