Both tools connect the same wide range of apps; the real difference for a two-person team is how fast you can get your actual workflow running, not which one has more integrations on paper. We set up two real workflows in both tools to compare: a simple order notification, and a branching supplier-reply router (from the supplier outreach playbook).
Workflow 1: New Shopify order → Slack + spreadsheet
A linear, no-branching workflow: new order comes in, post to a Slack channel, log it in a sheet. Zapier's step-by-step builder had this running in a few minutes; for a workflow this simple, its linear format is genuinely faster to set up than switching between canvas views.
Workflow 2: Supplier reply → branch by quoted price → route to a list
A reply comes in, gets summarized by AI, and then needs to go to a different list depending on whether the quoted price is above or below a target. This is where Make's visual canvas paid off: seeing the branch as an actual fork in a flowchart made it easier to check the logic was right before turning it on, versus reading through a linear list of steps and mentally tracking which ones were conditional.
Where the two felt similar
Both handled the AI-summarization step (calling out to ChatGPT or Claude mid-workflow) without issues, and both have a visual run-history log that made debugging a failed run about equally easy.
Our take
For a workflow that's genuinely linear (trigger, then one or two actions, no conditionals), Zapier is faster to stand up and easier for a non-technical teammate to read later. Once a workflow needs a real branch or you're chaining several conditional steps together, Make's canvas is worth the slightly steeper learning curve. A two-person team doesn't have to pick one exclusively; it's reasonable to run simple notification Zaps in Zapier and save Make for the handful of workflows that actually branch.
Zapier or Make for this workflow? A quick checklist
Ask these questions about each workflow, not about your business as a whole:
- Does it branch? If the next step depends on a condition (a price above target, a country, an order value), lean towards Make. If it's trigger, then one or two actions, lean towards Zapier.
- Who will edit it later? If a non-technical teammate needs to read or tweak it, Zapier's linear list is easier to follow.
- How often will it change? A workflow you're still adjusting every week is easier to reason about where you can see its whole shape at once. For a branching one, that's the Make canvas.
- Does it call AI in the middle? Both handled the AI-summarisation step fine in our setup, so this doesn't decide it on its own. What matters is how you use the AI output (see the prompt below).
- What happens if it fails silently? If a missed run means a missed order notification or a supplier left waiting, set up a failure alert to a real person, whichever tool you choose.
- Is it already running somewhere else? Don't build the same trigger in both tools. Two workflows firing on the same new order means duplicate messages and duplicate rows.
Map the workflow on paper before you build it
Most of the time a two-person team loses on automation goes on rebuilding something that was never clearly defined. Write the workflow out in plain text first and keep it in a shared doc; it doubles as documentation for whoever has to fix it later.
Workflow name: [NAME]
Trigger: [WHAT STARTS IT, e.g. new Shopify order]
Steps:
1. [ACTION, e.g. post order summary to #orders in Slack]
2. [ACTION, e.g. add a row to the orders sheet]
Branches: [IF ... THEN ... OTHERWISE ...] or "none"
Data needed at each step: [FIELDS, e.g. order number, customer country, total]
Who gets told if it fails: [PERSON / CHANNEL]
Owner: [WHO MAINTAINS IT]
Tool: [Zapier / Make], because [ONE-LINE REASON]If the Branches line says "none" and there are only a couple of steps, that's a Zapier job. If you find yourself writing nested "if... then if..." lines, it belongs in Make.
A prompt for the AI step in a branching workflow
In the supplier-reply router, the branch only works if the AI step returns something the next step can test. A friendly paragraph of summary can't be routed reliably; a fixed set of labelled lines can. This prompt is written for that job:
Summarise the supplier reply below. Return only these lines, in this order, with no other text:
MOQ:
Unit price: (as quoted, with currency, or "not stated")
Lead time:
Payment terms:
Price vs target: ABOVE / BELOW / UNCLEAR (our target unit price is [TARGET PRICE] at [TARGET QUANTITY] units)
Unclear or missing:
Do not round, convert or estimate any number. If the price depends on quantity and there's no price for [TARGET QUANTITY] units, write UNCLEAR.
Supplier reply:
[REPLY TEXT]Then branch on the "Price vs target" line: ABOVE goes to the "needs negotiation" list, BELOW goes to the shortlist, and UNCLEAR goes to a person to read. That third route matters. A summary can miss a caveat buried in the original wording, so anything the model isn't sure about should land in front of a human, not in a default list.
Worked example: a fictional two-person store's first two workflows
Take a fictional two-person store, "Brindle & Co", selling dog accessories and sourcing a new range of leads and harnesses, as an example.
- Order notifications in Zapier. New Shopify order, post to Slack, log to the orders sheet. The paper map has no branches and two steps, so it goes in Zapier, where either founder can read it at a glance.
- Supplier replies in Make. Each reply from the dozen suppliers they've contacted goes through the AI step above, then splits three ways on "Price vs target". The canvas shows the three routes side by side, so they can check that UNCLEAR really goes to a person before switching it on.
- What a person fixes after the first runs. In a setup like this, a typical early problem is the AI step answering "below" in lower case when the branch is checking for "BELOW", so the reply falls through to no route at all. The fix is either to tighten the prompt or to loosen the filter so it accepts either case, then check the run history to confirm that the next few replies land where they should.
- What stays manual. Follow-up emails to suppliers are drafted by AI but sent by a person, as the supplier outreach playbook recommends. Neither tool sends anything to a supplier on its own.
Common mistakes small teams make with Zapier and Make
- Using a canvas for a two-step notification. It works, but it's slower to build and harder for a teammate to read than a simple Zap.
- Forcing a branching workflow into a linear list. You can end up with conditions scattered through the steps that nobody can follow six weeks later.
- Routing on free-text AI output. Ask the model for fixed labels and route on those.
- No alert when a run fails. Automation is only worth it unattended, and unattended only works if failures reach a human, a point our n8n vs Make comparison makes as well.
- One person holds all the knowledge. Keep the paper map for every workflow in a shared doc, so a holiday doesn't turn into an outage.
- Not looking at run volume. Both tools bill by execution volume, so check the run history after the first week to see how often each workflow actually fires before the bill surprises you.
FAQ
Which one is cheaper? Both bill by execution volume with free tiers for light use; check current pricing on each site directly, since it changes and depends heavily on your run volume. (Checked 29 Oct 2026: Make's pricing page says credits are now its billing unit, with each action a scenario performs consuming credits, and Zapier's help centre says a task is any successful action that runs in Zapier, with triggers never using tasks; see Make pricing and How is task usage measured in Zapier?.)
Can you migrate a workflow from one to the other later? Not automatically. There's no direct import between them, so a workflow built in one has to be rebuilt by hand in the other. Worth deciding upfront which tool a given workflow belongs in rather than switching later.
Is Make harder to learn than Zapier? A little, in our experience. Zapier's step-by-step builder is quicker for a linear workflow, while Make's canvas takes slightly longer to get comfortable with. That learning curve pays off once a workflow has real branches, because you can see the logic instead of tracking it in your head.
Should a two-person team use both Zapier and Make? It's reasonable, as our take above says: simple notifications in Zapier, the few branching workflows in Make. Just keep a note of which workflow lives where, and never build the same trigger in both.
Can Zapier or Make send AI-written replies to suppliers automatically? They can be set up to, but we wouldn't. Pricing and payment terms are exactly where a wrong assumption or a mistranslated number costs real money, so let automation log and route replies, and have a person read and send anything going to a supplier.
When should a small team look at n8n instead? Mainly if you have a real data-residency requirement, stable high volume and someone who can maintain a server. Our n8n vs Make comparison for small teams walks through that decision.
See Zapier and Make directly, or the supplier outreach playbook for the full branching workflow this comparison was based on.
