What Actually Matters When You Use Make Between Teams

1. Scenario triggers run differently than webhooks and nobody tells you

One of the first weird things you’ll hit in Make.com is this sneaky difference between webhook triggers and scenario triggers (like “Watch Rows” from a Google Sheet or “New Record” in Airtable). You’d think they’d all behave roughly the same, but nope — webhook triggers fire instantly (ish), while most of the built-in polling ones check on a fixed schedule… which you can change, but sometimes it resets unpredictably if copied or renaming the module.

I had a Google Sheet trigger set to watch for new rows. It worked great during testing, especially when I mashed the manual run button. Then I made a copy of the scenario to change outputs, and somehow the interval set itself back to 15 minutes. I missed incoming data for like two days.

The polling triggers in Make mostly live under the hood adjusting timestamps, and don’t leave visible logs until data hits. That makes them both mysterious and dangerous if you’re troubleshooting failed or late runs.

This isn’t in the documentation clearly — the polling behavior is based on an internal last-run timestamp that’s surprisingly fragile. Webhooks, on the other hand, are much easier to debug because you literally see each hit and have data snapshots.

The key: when you’re building anything time-sensitive, use webhooks over polling. And if polling is unavoidable, add a test notifier inside the triggered path that confirms it ran — even if you just send a Discord message saying “Google Sheet scenario ran at {{now}}” before you touch real data.

2. Scheduling modules can delay output without warning or logs

There’s a sneaky bug you’ll eventually see if you schedule scenarios to run at custom intervals, like hourly or every 15 minutes. I had one that was set to run hourly, parsing Slack channel messages and updating an Airtable log. One day I noticed updates were delayed — like 20 to 40 minutes late — but Make.com showed the scenario started on time.

Here’s the trick: if another scenario is running on your Make.com org and chewing heavy memory (CSV parsing, big Airtable syncs, any “Search All” loop), the scheduled scenario may show as “started” but not actually begin work inside until later. There’s no error, just delayed execution. No logs to confirm the lag — the timeline lies.

I only confirmed this by stacking three identical scenarios on different orgs and watching the same trigger behave fine on the less-busy ones. The lag matched the memory load, not the clock.

A few fast tips to dodge this

• Use webhooks for real-time triggers if data freshness matters
• Add a 1st module that logs the actual {{now}} timestamp to somewhere visible
• Move heavy data transform steps into separate scenarios triggered downstream
• Don’t assume timelines in Make mean what you think they mean
• Set memory alerts in your Make org dashboard if you’re on a paid tier

3. Aggregator modules quietly change keys unless explicitly mapped

This one had me tearing open my dataset at 2am: when using aggregator modules (like Array aggregator or JSON aggregator), if you leave any of the field mappings as “map all fields,” Make doesn’t always preserve key names. I had a text clean-up scenario that looped through scraped descriptions and re-packed results into a collection.

The original keys were fine in test mode. But in live mode, when the input data structure shifted slightly — like when one item had an extra custom field — Make dropped several expected fields and renamed one from “title” to “title 1” without any warning.

The bulk aggregator modules re-infer keys on each run unless your mapping is hard-set per field. That’s the quiet chaos: everything works until an edge case hits, and then your downstream steps (which rely on exact keys) break without an error, because the module still technically completed.

If you see strange field names like “name 1” or “email 2,” it’s the aggregator silently re-identifying duplicates.

My fix was to stop using auto-map and bite the bullet — manually map every key even if feels annoying. Static mapping means stability. Bonus tip: add an “if empty then default to ‘missing’” string next to each mapping if the input might be sparse.

4. Airtable keys shift if you reconnect apps between teams

This one stung hard during a client handoff. We’d built a dozen Airtable automations using their account tokens. Everything worked flawlessly… until they moved the Airtable base to a different workspace and reconnected the Make.com apps. Suddenly several modules failed silently — not with error messages, but by returning empty arrays.

The problem? Make.com identifies Airtable tables by their internal base and table IDs, not by names — and if you move a base between teams inside Airtable, the IDs change. Even though the schema looks the same (columns, views, etc), Make now can’t find the table, and doesn’t crash… it just returns nothing.

We confirmed this by opening the Run log and looking at the API response — the GET succeeded, status 200, but the `records` array was blank. It knew the base existed, just not the table anymore. It was expecting `tblxDFkj3` but found only `tblkXjZ29`.

The workaround: whenever you duplicate or move Airtable content between teams, reconnect the Airtable module from scratch and reselect the base and table manually. Re-authenticating isn’t enough — the internal ID must be re-picked.

5. Router branches execute in weird orders and it depends on structure

This one started when a collaborator moved a branch on a Router in our Make.com scenario. We had three outputs: one to Slack, one to Notion, one to a webhook. Everything had worked fine — but after reordering them visually, we started seeing webhooks fire before Notion updated. Extremely weird timing issues. Turns out Render Order isn’t guaranteed.

In Make.com, the Router’s outputs aren’t processed in left-to-right UI order. Instead, it builds an execution tree depending on when the branches were created. Sequence depends more on module lineage than visual layout. So if you delete and readd a path — even in the same position — Make might treat it as a new execution edge, and reorder it.

Router branches don’t fire in parallel unless the inputs are fully independent. Even if they look like siblings, delays in one can impact the others downstream.

I used logging modules to force each branch to send a timestamped report before doing anything else. That’s how we realized outputs were out of sync. If the webhook gets a timestamp 5 seconds before the Notion update starts, you’ve got race conditions.

Fix: if order matters, don’t rely on Router branches. Split the paths with sequential modules and use filters instead. Or manually serialize the actions — even if it’s less elegant, it’ll behave more predictably.

6. Unexpected array wrapping inside HTTP response modules

This one’s straight from a forehead-slap moment. If you use an HTTP module to call a JSON API, and that API returns a single object, Make often parses it fine. But if the API sometimes wraps the object inside an array — even when there’s only one element — Make flips the data type silently.

I had a scenario pulling from a financial API that returned transaction data. Half the time, it came as a single object. Other times — usually when the amount field had fractions, bizarrely — the same endpoint returned an array with one object inside. Make didn’t warn me. It just ran a search expecting `response.amount` and got `undefined`, because now the correct field was `response[1].amount`.

This doesn’t create a failed run. It creates a success with nonsense data.

Quick workaround:
Wrap the HTTP response in a Repeater set to 1 iteration, then handle `.value[1]` with error catching to normalize the structure. Or hit the JSON module just after and flatten the array if exists, like:

{{if(exists(response[1].amount); response[1].amount; response.amount)}}

This let me keep the same logic without building two paths.

7. AI modules hallucinate fields unless filtered downstream

There’s this cheerful OpenAI module inside Make.com that looks really handy: send in text, get structured JSON back. Sounds great until you use it inside a map scenario, pump in 20 records, and the AI goes completely unhinged on record 7 and adds keys you never trained for.

One test I ran was classifying customer feedback into tags using GPT-4. First six items tagged cleanly into “delay,” “pricing,” or “support” buckets. But then one weird message — about shipping times at a particular ZIP code — created a brand new “logistics_dead_zone” key in the output JSON. That busted the aggregator I had later in the path.

Turns out OpenAI doesn’t always respect fixed schema boundaries inside Make unless your prompt pins them super tightly. Any outlier can introduce unseen keys that break downstream steps — and Make doesn’t coerce AI output into standard structures. JSON validation doesn’t help much either; it’ll just pass through the key explosion.

My move now is to buffer all AI outputs through a manual mapping stage right after the OpenAI module. I only forward fields I want via explicit mapping — even if it’s slightly redundant. Also helpful: do similar prompts through Zapier first to see how their AI flow behaves. They sometimes normalize outputs more aggressively.