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How to choose between Make, n8n, Zapier and building it natively

The platform matters less than where you put the logic. Here is the decision method, and the question that settles most of these arguments in about a minute.

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Quick answer

Should I use Make, n8n, Zapier or native automation?

Build inside your CRM when the logic only touches CRM data, since native automation has no integration to break. Use Zapier for simple two-app connections, Make for multi-step branching logic, and n8n when you need self-hosting, data residency control or genuinely custom code.

Most of these debates are really about something else. The platform question is downstream of a architecture question nobody asked out loud.

The question that settles it

Does this logic need to know about anything outside the CRM?

If no, build it natively. You remove an authentication token that can expire, a third-party that can have an outage, a per-operation cost, and an entire class of debugging where the answer is in someone else’s execution log.

If yes, then you are choosing an orchestration platform, and the choice is mostly about complexity tolerance and who maintains it.

The rule that prevents the worst outcome

One piece of logic lives in exactly one place.

The genuinely painful stacks are not the ones using the wrong platform. They are the ones where half a workflow is in the CRM, half is in Make, and a webhook in the middle connects them — so diagnosing any failure requires reading two execution histories and inferring what happened in between.

Split by responsibility, never by convenience. The CRM owns CRM logic. The orchestrator owns cross-system movement. Nothing straddles.

What actually differentiates them in production

Not the trigger catalogue — they all cover the common systems. What differs:

  • Error visibility. How quickly can you see that something failed, and why?
  • Replay. Can you re-run a failed execution after fixing the cause, or is that record lost?
  • Data shaping. How much work is it to transform between two systems’ formats?
  • Version control. Can you diff a change, and can you roll one back?

Evaluate on those four with a real automation, not on a feature grid.

The steps, in order

  1. Step

    Write the automation down in plain sentences first

    Trigger, conditions, actions, and what should happen when each step fails. Platforms are chosen on capability, and you cannot assess capability against a requirement you have not written. Most platform arguments dissolve once the requirement is on paper.

  2. Step

    Check whether it can be native

    If every system involved is your CRM, build it there. Native automation has no authentication to expire, no third-party outage to absorb and no per-operation cost. The temptation to reach for an external tool because its builder is nicer is real and usually wrong.

  3. Step

    Count the apps and the branches

    Two apps and a straight line is Zapier territory and there is no shame in it. Several apps, conditional branches, loops over arrays, or data reshaping between steps is where Make earns its complexity.

  4. Step

    Decide where the data is allowed to live

    If regulation or a client contract restricts where customer data may be processed, self-hosting stops being a preference. n8n can run on infrastructure you control; the hosted platforms cannot. Settle this before comparing features, because it eliminates options outright.

  5. Step

    Specify the error behaviour for every step

    What happens on a 500, on a timeout, on malformed data? Retries with backoff, a dead-letter path, and an alert somewhere a human actually looks. An automation without defined failure behaviour will fail silently, and silent failure is worse than no automation.

  6. Step

    Work out the real running cost at your volume

    Pricing models differ fundamentally — per task, per operation, per execution — and a multi-step scenario can consume many operations per run. Model it against your realistic monthly volume rather than the sticker price, because the ranking frequently reverses.

  7. Step

    Decide who maintains it in a year

    The most capable platform is the wrong choice if nobody on the team can operate it. Be honest about who will debug this when it breaks and you are unavailable. This consideration should outrank feature comparisons more often than it does.

  8. Step

    Build the smallest version and run it in production

    One trigger, one action, real data, for a week. Every platform demos well. What you are testing is the reliability and the error visibility, and neither is observable in a sandbox.

  9. Step

    Add logging before adding complexity

    Whatever you choose, make the execution history reachable and make failures visible to someone. An automation nobody can see is an automation nobody can fix, and complexity added before observability compounds that.

Questions

Related questions

Is n8n always cheaper than Make or Zapier?

Not once you count operational cost. Self-hosting removes the subscription and adds hosting, updates, monitoring, backups and someone responsible for all of it. At low volume the hosted platforms are usually cheaper in total. At high volume, or where data residency is a requirement, self-hosting wins.

When should automation live in the CRM rather than an external platform?

Whenever the logic only touches data the CRM already holds. Native automation cannot break through an expired token or a third-party outage, and it costs nothing per run. Reach outside only when you genuinely need a system the CRM does not connect to.

What is the most common mistake when choosing?

Choosing on the builder interface rather than on error handling. Every platform looks capable when the happy path runs. The differences that matter appear when a step fails at 3am, and that is what should be evaluated.

Can these platforms be combined?

Yes, and most mature stacks do. Native automation handles CRM-internal logic, an external platform handles cross-system orchestration. The rule worth keeping is that any single piece of logic should live in exactly one place — logic split across two platforms is the hardest kind to debug.

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