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Why I Split One Make.com Automation Into 11 Separate Scenarios (And Why You Should Too)

  • 3 days ago
  • 4 min read

When I first started building content automation pipelines in Make.com, I did what most people do: I tried to cram everything into a single scenario. Pull the RSS feed, scrape the article, pick the products, rewrite with AI, generate images, publish to Shopify, post to Pinterest, blast it on Twitter and Facebook. One flow. One trigger. Done.

That worked great. Right up until it didn't.


After a few weeks of watching the whole thing collapse because a single Scraptio timeout tanked a Pinterest publish that was supposed to go out three hours later, I sat down and redesigned it from scratch. The result was an 11-scenario modular pipeline, all sharing a single Make Data Store as a state machine. And I have not looked back.


Here is what I learned.

The Monolith Problem

A single long scenario in Make.com looks clean on paper. One canvas, one flow, easy to trace. But the moment you have 20-plus modules chained together, you are basically running a distributed system with zero fault isolation.


If module 7 fails, modules 1 through 6 have already burned their operations. If the AI call in the middle times out, your Shopify publish never fires. And good luck figuring out where exactly it broke when Make shows you a generic error in the middle of a 30-module canvas.


This is the same reason nobody ships monolithic backend services anymore. Not because microservices are trendy, but because blast radius matters.



The Data Store as a State Machine




The architecture shift that made everything click was treating the Make Data Store not as "temporary storage" but as a proper state machine.




Every article in the pipeline has a status field. Here is what that field looks like across its lifecycle:

new -> products_ready -> draft_ready -> html_ready -> published -> pins_ready -> pins_published -> social_ready -> done

Each scenario does one thing: it searches the Data Store for records in a specific status, processes them, and then writes the next status back. That is it.


  • Scenario 1 pulls from RSS, scrapes the full text, extracts keywords with GPT, and saves with status: new.

  • Scenario 2 picks up status: new records, finds 3 matching products from the catalog using semantic matching, and updates to status: products_ready.

  • Scenario 3 picks up status: products_ready, rewrites the article with AI, injects product placeholders, and pushes to status: draft_ready.

And so on down the chain.

This pattern is not new. It is exactly how job queues work in backend engineering. Bull, Sidekiq, and Celery all follow the same principle: work units move through states, and each worker only cares about its slice.



The Real Benefits You Feel After Week Two

Error isolation is the big one. When the image generation API goes down (and it will), only Scenario 4 fails. The article is still being drafted. The products still get matched. When the API recovers, you just re-run Scenario 4 against records stuck in the html_ready limbo. No data loss, no re-processing, no burned operations upstream.


You can iterate on any single stage without touching the rest. Last month, I completely rewrote the prompt in Scenario 3 to improve the article structure. Changed nothing else. Ran it against three test records, liked the output, shipped it. If this were a monolith, changing one prompt would require retesting the entire 30-module chain.


Debugging becomes actually useful. When Scenario 6 throws an error on a Pinterest pin, I open Scenario 6, check the execution log for that run, and see exactly which module failed and what data it received. Clean, isolated, readable.


Scheduling flexibility is underrated. Some stages are fast and cheap, some are slow and expensive. Scenario 1 runs every 15 minutes. Scenarios 3 and 4, which make multiple calls to AI APIs, run every hour. The social posting scenarios run on a custom schedule based on optimal engagement windows. You cannot do any of this granular scheduling with a single monolith.


Operations costs become predictable. Because each scenario only activates when there is a matching record in the Data Store, you never burn operations processing something that has nothing to process. The whole pipeline is pull-based, not push-based.


What the Pipeline Actually Looks Like

For context, here is the full chain I am running for the HyggeCave content pipeline: https://hyggecave.com/

  1. RSS + Scraptio + Save to Data Store

  2. Select 3 Products for Article

  3. Generate Draft Article (AI Rewrite + Placeholders)

  4. Render Products + Image + Final HTML

  5. Publish Article to Shopify

  6. Generate Pinterest Pins (Text + Image Prompts)

  7. Generate Pinterest Images + Save to Data Store

  8. Publish Pinterest Pins

  9. Publish Twitter Post 1

  10. Publish Twitter Post 2

  11. Publish Facebook Post

Total: 11 scenarios, one shared Data Store, zero single points of failure.

Each scenario is independently testable, independently schedulable, and independently debuggable. When I hand this off to someone else on the team, they can understand one scenario at a time without needing to hold the entire pipeline in their head.


When to NOT Split

Not every automation needs this treatment. If your scenario has fewer than 8 modules, no external API calls that can fail independently, and you will never need to rerun individual stages, keep it in one flow. The overhead of managing multiple scenarios is real.

But the moment you have AI calls, image generation, third-party publishing APIs, or any operation you might want to retry independently, split it. In the future, you will be grateful.



The Takeaway

The instinct to keep everything in one scenario comes from the same place as the instinct to write one giant function that does everything. It feels simpler. It is not.

Splitting your Make.com automation into modular scenarios with a shared Data Store state machine gives you fault isolation, independent iteration, clean debugging, and scheduling granularity that a monolith simply cannot provide. It is a bit more setup upfront. The operational benefits start paying off immediately.

Build for the second week, not just the first run.

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