Zapier vs Make for Automation Under Fifty Bucks Monthly
1. Comparing free and basic paid tiers from actual usage
Most people run into this at the same spot: you’re about 14 Zaps deep in Zapier before you realize the free tier’s not gonna cut it, and “Starter” gives you what—20 Zaps and 750 tasks a month? “Tasks”, by the way, includes every single SMS you forgot your automation sends twice because you debugged it with “Test Zap”.
With Make, the pricing kicks in differently. You get unlimited scenarios on the free plan but only 1,000 monthly operations and 100MB data transfer. So yeah, technically unlimited workflows—you just can’t run them very far. If you drag in any API-heavy module upstream (e.g. Webhooks + Airtable), a single run can burn through 12+ operations. Easily.
About two months ago, I downgraded both platforms to basic paid plans (Zapier Starter and Make Core). Cost-wise, it balances out to under 50 total if you don’t go over quota too often. What’s noticeable is Make’s pay-as-you-overuse pricing—feels weirdly fair but also easy to accidentally trigger when building with loops.
A little moment: My first surprise bill came from Make, not Zapier. I set up a looping webhook to retry a Monday.com task fetch when it times out. The bad part: it never timed out. The retry loop triggered anyway, eight times every 10 seconds. 2700 operations. One day.
2. Visual UI differences that actually affect debugging speed
Zapier’s UI is straightforward until it breaks. When a Zap fails mid-step, you get a nice red box with “Something went wrong”, then a not-that-helpful traceback unless it’s a pure 401 or 404. There’s no real “execution tree” or data replay. You’re mostly left hopping back from the Task History window trying to guess context.
Make’s visual builder is useful once you learn what bubbles mean. It shows the whole scenario as a flowchart, and when something throws an error, you can watch the exact branch where it hiccuped. But early on, I made the mistake of not turning on “Break error” on a failing module — Make just skipped the failing block and ran the rest, silently.
Debug lesson:
// Received JSON payload from webhook, expected key was 'data.rows.n'
{"data": {"n": 367}} // Make fail
// Expected
{"data": {"rows": [{"n": 367}]}} // Make worksIn Zapier, it refuses the run outright and throws validation errors if anything doesn’t parse properly. Make just gives you empty bubbles unless you dig into the run log and open bubble #4 directly. Very visual, also very cryptic when you don’t know what you’re looking at.
3. API flexibility and webhook behavior differences
Zapier’s built-in webhook trigger seemed perfect, until I set up a multi-step process that fired to Zapier and then from Zapier, re-posted to another webhook endpoint. And it ran that step twice. Not joking — the task log showed a single incoming payload, but double outbound executions.
Zapier support actually wrote back with this detail: when a webhook trigger receives a fast-follow POST within a few milliseconds–e.g., same microservice chunked into two—it tries to buffer both and can re-emit under rare load balancing conditions. Translation: it thinks you double-clicked. There’s no setting to throttle this. I had to build a flag into the incoming body and explicitly block duplicate payloads with a Python code step.
With Make’s webhook module, you register a single scenario endpoint, map the expected structure, and it absolutely refuses any JSON it doesn’t recognize. This is great for structured APIs, terrible if your webhook source changes field order or occasionally omits a field. It’ll just… not trigger. No error, no bounce logged. You realize three days later when your CRM has 17 missing contacts.
4. Unexpected behavior when looping or iterating in workflows
Make supports iterators and aggregators as actual modules. Zapier doesn’t—it kind of fakes loop behavior using an “Action: Code” step or via Paths. If you want to send 10 different Slack messages from a Zap, you’re either using Looping by Zapier (which is experiment-level unstable) or coding a chunk of JavaScript to spit out arrays of outputs.
I once used Make’s Array Aggregator to collect task labels from ClickUp, then fed those labels into OpenAI for summary strings. For some reason, the aggregator occasionally dropped exactly one array member per 8+ sourced elements. The missing one? Consistently the second-to-last. Couldn’t log it until I wrapped the array with a two-pass JSON extractor and did a before/after diff. Edge case? Yes. Still happens. Make doesn’t normalize undefineds the same way across modules.
Practical loop tip block:
- In Make, wrap iterator array outputs with JSON.stringify → log → parse again to confirm shape
- In Zapier, use Google Sheets as a fake loop driver by inserting rows and triggering on row updates
- Set a sleep or delay inside any Make scenario using recursive webhooks (avoids quota floods)
- If you’re looping user API IDs in Zapier to unsubscribe people, double-check Zapier dedupes IDs
- Zapier Paths still charge tasks for skipped branches
- Make aggregators silently skip nulls unless explicitly defined as “Empty string” in mapping
5. Cross-platform integrations and rate limiting differences
At a glance, both platforms support a massive list of apps. But underneath, Zapier’s connections rely on stricter API credential pairing—you generally pick OAuth or API Keys, and switching between them mid-Zap? Not possible without recreating the connection.
Make feels hackier to build with because you can inject HTTP/API requests directly alongside official modules. That’s also how I broke my Monday board: using Make’s raw HTTP module to ping their API faster than Zapier ever could. Except Monday restricts burst calls and doesn’t surface useful error verbosity. They bounced four of my writes with status code 429 and no body at all.
“Rate limit hit — Retry after: undefined” — Monday.com response to Make HTTP module
Zapier batches tasks slower, but more reliably. And their error messages in connected apps are cleaner. If Notion refuses your write, Zapier shows you the exact database title mismatch. Make? You’ll just see a red bubble with “Write failed” and nothing helpful unless you manually open Data → Output → Raw response.
6. Real team-member responses to each platform’s workflow UI
Here’s what actually happened: showed the Zapier builder to two marketing folks who’d never automated anything. Five minutes in, one of them built a “Slack me every time X happens” flow—and it worked. Zapier’s UI wins in sheer lower-friction startup.
Same test in Make? Confusion. What’s a module? Why are there bubbles? What’s a router? It’s not obvious what order things happen unless you’ve seen scenario logic before. I had to draw a whiteboard version of the flow just to explain where the condition split the modules.
But—when someone from engineering looked at the same Make scenario, they immediately asked: “What happens if this branch fails? Can I retry just B, not the whole thing?” That’s the sweet spot. Make workflows are easier to debug like real code if your team thinks like developers.
7. Data formatting quirks and expression language mismatches
You haven’t felt pain until you try to format dates from an international Shopify order (e.g. 05/03/2024—is it May or March?) and send them to an Airtable column that breaks if milliseconds are included. Zapier uses its own flavor of string manipulation—FORMAT() functions and date transformers. Make has inline JavaScript-style expressions and parsing via Moment.js clones.
Biggest mismatch? Zapier fails fast but doesn’t tell you why. I had a webhook sending an ISO 8601 datetime. Zapier tried to reformat it to “Month Day, Year” and just ended up using today’s date instead. No error, no fallback notice. Output looked fine until someone pointed out all the dates were the same.
Make lets you write mapping expressions using variables like {{formatDate(now; "YYYY-MM-DD")}} or {{addDays(now; 3)}}, but if you mistype the format or delimiter, you just get the raw input string. No hint that your expression failed.
Also note: neither handles leading zeros consistently. I had to pad single-digit IDs with LPad functions in Make, while Zapier rounds numbers unless explicitly cast to text type with a Formatter step.
8. Choosing based on what breaks the least often mid-month
I track failures using synced error logs in Notion, flagged by a custom field if the error repeats twice in the same week. Zapier routinely shows failed tasks from expired tokens or outdated trigger settings after third-party app updates (e.g. when Google Form structure changes). Make errors more often from malformed schema expectation—especially if someone edits a module after saving a mapped field.
Most common silent error in Zapier: Gmail apps throttle on consumer accounts mid-month, and no warning appears until you hit send limits. Make shows the 429 errors instantly, but lets you keep retrying (and burning tasks) without success. One time, my scenario retried a failed Notion write eight times before exiting, using nearly all my operation quota for the day.