Zapier vs Make for Solo Founders Trying to Automate Fast

1. Differences in how triggers behave across Zapier and Make

On Zapier, most of the time your trigger feels like a black box—you set it to watch for a new row in Airtable, or an updated deal in HubSpot, and for the most part, it works. Until one day you realize it didn’t. The row updated, the zap didn’t fire, and now you’ve got a bunch of Slack notifications missing for something your client thinks you knew about. That tactile feeling of clicking “Test Trigger” in Zapier and getting “We could not find any new items” even though there definitely are new items—that’s part of the ride.

Make, on the other hand (formerly Integromat), gives you more technical rescue ropes. You can grab bundles directly, evaluate timestamps, and rerun previous modules with a downloadable log. But you also get hit by a different flavor of frustration: silent failures from misconfigured filters or unhandled data structure forks. I once sat staring at a scenario in Make that was completely green—no errors, all modules processed—and yet a record never made it to Google Sheets. Spoiler: the iterator’s output path was backward, resulting in an empty array silently skipping everything downstream.

The core difference here is opacity. Zapier tries to shield you from complexity, often hiding the inner wirings until you’re digging through Task History. Make shows you the plumbing, which means you can fix things faster—or screw it up more technically.

2. Handling branching logic and conditional workflows realistically

Zapier’s conditional workflows (Paths) feel like they were bolted on years after launch and never fully adopted by the core editor. You can use Paths with AND/OR logic, but once you start nesting conditions—like trying to route different email types to different templates—you’ll burn time clicking between micro-UIs and wondering where your context went. And the 3-path limit on the free plan isn’t even the worst part; it’s the fact that reordering or restructuring Paths requires deleting and rebuilding chunks manually.

Make eats this for breakfast. You can visually fork a scenario anytime, as many branches as you want, and every branch can carry its twiggy little array of custom filters and conditions. It looks a bit like a series of cartoon plumbing tubes, but when you see three bubbles going left, two going right, and one going nowhere? That’s data clarity. The annoying bit is this: Make’s logic chain works left-to-right, then top-down, and filter conditions can stack in super confusing orders if you drag modules out of sync. An edge case? If you rename a mapped variable but forget to regenerate downstream filters, they silently fail. I had one email module that stopped firing because a single variable field switched from customer_name to client_name upstream. No error. Just skipped.

If you’re trying to build a decision tree that branches five or six ways, Make wins outright. But if you need fast, one-off logic (“If this form = X, send this text”), Zapier’s more efficient for small pipelines.

3. Working with webhooks in both platforms under pressure

The first time I added a webhook trigger in Zapier, I realized there’s no built-in way to replay old webhook submissions. You get one shot: send the request, hope Zapier catches it, then test from there. If that webhook came from a production app with a UUID payload you can’t fake? You’re now playing Postman musical chairs, pasting raw JSON back and forth manually. Honestly, webhooks in Zapier feel like they were designed for minimal use—great if you just want to plug in Stripe or Shopify, limited if you’re building from scratch.

Make’s webhook module looks ugly on first click but turns out to be way more flexible. You can inspect every payload, store past hits, and even route them through routers and iterators immediately. There’s this neat little feature called “Custom Webhook Response” that lets you format the HTTP 200 response directly, which is mandatory if you’re working with Slack interactivity or anything API-recursive. The weirdest bug I found? Make sometimes fires the webhook twice if you test it manually too quickly in succession—especially if your browser autocomplete resends POSTs with the same payload. Took me an hour to figure out why my Discord messages were showing up twice. Hint: the replay button on the webhook history panel doesn’t always reflect real-time delay behavior.

For speed and debugging visibility, Make takes it. For plug-and-play with popular SaaS? Zapier’s still easier if the integration exists.

4. Managing API rate limits and throttling behavior in real scenarios

This came up hard while helping a coaching client sync Circle community members with ConvertKit tags. ConvertKit has conservative rate limits, and Zapier would start throwing 429 errors randomly whenever the daily bulk tag logic ran. The zap would retry—but only once, after what felt like 30 seconds—and then quietly fail. No notification unless I checked “Zap Runs.” It’s like, cool, your automation died but didn’t want to burden you.

Make doesn’t retry by default, but it does let you handle throttling structurally. There’s a Sleep module that lets you build in backoff timing, plus error routes where you can map 429 responses to alternate outcomes. So instead of retrying once and dying, you can actually say “Okay, wait 10 seconds, try again, and if it fails three times, write to Airtable.” That saved my life when working with the Notion API, which rate limits aggressively. But what’s even trickier is this: if you run Make scenarios manually via the play button, you will not hit rate limits—but if you run them on schedule with multiple operations batched, they hit way faster. Same payload, different result, based on launch method. That’s not in their docs, but I confirmed the behavior by logging timestamp intervals on retries.

If API timing matters to your situation at all, Make is where you can actually manage it. Zapier’s retry logic is hidden and inconsistent depending on the connector.

5. Historical run logs and actual debugging differences

This is where it stops being subjective and gets painfully obvious. Zapier’s Task History shows a streamlined log of what ran, what data entered a step, and what went out. But the entire thing is paginated inside a modal, and long zaps with 7–8 steps become an exercise in tab cycling. Worse, there’s no easy way to filter for failures across all zaps unless you’re on Zapier’s Teams-level plans and build alerting zaps yourself. Which… feels like a weird kind of recursion.

Make, on the other hand, has a full scenario-level run history with diagrams of every run. You can literally click into any bubble, inspect the payload, and re-run any single module. That’s saved me multiple times when dealing with Edge cases. Once, a scenario stopped emailing new signups just because one email contained an emoji that broke a regex pattern in a filter. I never would’ve caught that upstream in Zapier—Make showed me the filtered-out item instantly. Real quote from the log:

{“error”:”Invalid character ‘\uD83D’ in filter expression”}

Yes, filtering an emoji broke a filter. Only way I caught it? Scrolled through Make’s execution bubble and read the full error message aloud in disbelief.

Debugging clearly favors Make. But Zapier’s logs are cleaner when nothing breaks. So if your workflows rarely fail, Zapier’s simpler. If you’re spending hours debugging transforms and edge cases—go Make.

6. Comparing costs beyond just price per task or operation

This is where it really blew up for me. On paper, Zapier pricing looks steep: per-task, per-zap limits, and most of the cool “code step” stuff lives in higher plans. Make uses an operation-based model instead—run one module = one operation. But if a filter fails, that’s still one operation. And iterators can multiply operations fast. For one of my Airtable sync scenarios, I thought I’d built a lightweight setup—grab records, check field, send Slack. But because I was iterating over a nested array with four routes, each run turned into ~40 operations. Multiply that by 500 records daily and it broke the included Make quota in like eight days.

Zapier, meanwhile, only counted runs that passed filters and hit external apps. So ironically, despite being “more expensive,” it ended up cheaper for that workflow. What’s counterintuitive: Zapier penalizes wide workflows (lots of steps), Make penalizes deep workflows (nested iterators or arrays).

So don’t just count monthly pricing—simulate real runs. Open a spreadsheet and block out your scenario structure, then reverse-engineer rough pricing from historical volume. This is especially important if you’re a solo operator with unpredictable spikes from client launches or email sends.

7. Sharing workflows with clients or teammates mid-build

If you’re freelancing or building with teams, this gets awkward fast. Zapier doesn’t let someone else edit your zap unless you’re in the same Workspace, and multi-user access starts at Pro-level tiers. That’s fine until a teammate pings you on Slack asking “Where do I update the filter for new leads?” and you’re stuck screen recording walkthroughs. I once created a separate Dropbox folder just to store exported Zapier JSONs to manually diff edits with clients. It feels like overkill because it is. But there’s no better way unless you’re deep in their editor.

Make handles this better—sort of. You can share Scenarios inside teams, and the visual editor means it’s easier to talk through workflows over Zoom. Huge win there: duplicating a scenario into a sandbox account for testing is non-destructive. Weird caveat: if someone edits a webhook module or cuts a route midway through branching logic, it can delete variables everywhere downstream. No warning. No undo. Literally had a junior dev delete a router by accident and the entire scenario grayed out.

If you collaborate often, Make’s structure wins, but only if you’re strict about backups. I now duplicate every live scenario into a “shadow” version nightly—just because re-building filters from memory sucks.

8. Using code steps for faster data transforms or API juggling

Zapier’s Code by Zapier step is quietly powerful but sandboxed. You get NodeJS or Python, but only in a constrained runtime—no libraries, no imports, no external fetches. I used it once to parse a CSV blob from Dropbox into JSON so I could feed it into Coda. Worked—but error messages were cryptic if the script failed. If you’re comfortable writing try/catch wrappers around everything, it’s fine. Otherwise, expect a lot of “Execution failed: Error occurred on line undefined.”

Make supports scripting via their Tools modules (e.g. Text aggregator, JSON parse, etc.), but real code requires workarounds: connecting to an external webhook or using an external server. Not ideal for solo builders unless you already have a server running, or you play API middleman via Cloudflare Workers. That said, Make does support raw HTTP modules that let you do basically anything as long as you can craft the headers + payloads yourself. It’s like low-code Postman mashed into sequences.

• Use Zapier’s code step for one-off transforms that don’t require external calls
• Use Make when you need to dynamically fetch data mid-scenario (e.g. API call → iterator → dedupe)
• Test any transform outside the platform first—both editors eat bad inputs with vague errors
• Wrap every conditional script with fallback values—null in Zapier, empty object in Make
• Never assume console.log output shows up live—it’s delayed or dropped in both platforms sometimes

Honestly, a code step is less about power and more about whether you’ll be awake if it fails silently at 3am.