Zapier AI: What I Actually Automate, and Where Make Won Me Over

In short: Zapier connects apps into automated workflows (Zaps), and the AI by Zapier action drops an LLM call into the middle of a flow to read, summarize, rewrite or route unstructured content. That turns Zapier from a passive connector into a logic layer. For complex flows with loops and list processing, Make.com is the cheaper, more visual alternative, while GDPR compliance requires a signed DPA for European personal data.

I've been using Zapier since 2022, and when AI by Zapier landed in 2023, I shifted from treating it as a "connector" to treating it as an "agent." I've also moved part of my automations over to Make.com for concrete reasons I'll lay out here. Here's what these platforms actually do for me as of May 2026, after several hundred Zaps built, broken, and rebuilt.

My angle

I run about a dozen active automations full-time, plus roughly fifty scenarios across SMB clients. I track two things: real monthly access conditions and net time saved. When a Zap saves me less than an hour a month but burns 200 tasks, I kill it. That discipline turned my Zapier bill into a measurable ROI.

What Zapier with AI actually brings

Before 2023, Zapier was an "if X then Y." Today, the AI by Zapier action lets you drop an LLM call (OpenAI by default, Claude or others optional) into the middle of a workflow. In practice, I can:

• Read an incoming email and pull out a category, a sentiment, named entities.
• Summarize a long transcript and turn it into a structured action list.
• Rewrite an email draft to match the tone a given recipient expects.
• Decide on routing based on unstructured content.

That's the "tipping point": Zapier is no longer a passive connector, it's a logic layer.

My production workflows

Incoming lead triage. A prospect fills in a Typeform → AI by Zapier analyzes the description of the need → a qualification score between 1 and 10 → above 7, the lead goes into HubSpot with assignment to a sales rep; below that, it drops into an automatic nurturing sequence. Set up in late 2024, this workflow cut the time I spent sorting inbound requests myself by a factor of three.

Meeting summary to CRM. Zoom meeting ends → Whisper transcribes the audio → AI by Zapier extracts commitments made and next steps → adds them to Notion plus a recap email to participants. Payoff: roughly 30 minutes per meeting.

Support ticket triage. Incoming email to the support address → AI categorizes it (bug, question, billing, urgent) → assignment to the matching agent in Zendesk, priority adjusted according to the detected sentiment.

Multi-platform publishing. New article published → AI generates 3 social post variants tailored to LinkedIn, X, and the newsletter → scheduled publishing. I review and fix things before they go out, but the draft lands ready.

Zapier vs Make: why I migrated part of it

On simple workflows (fewer than 5 steps, little conditional logic), Zapier is still faster to build. The interface is better for someone who isn't a developer.

On complex workflows — loops, list processing, multiple branches, structure transformations — I moved to Make.com. Three reasons:

1. Execution access conditions. On Zapier, every step counts as a task. A 6-step Zap that runs 500 times a month burns 3,000 tasks. On Make, "operations" are noticeably cheaper for an equivalent volume.
2. Visualization. Make's graphical interface lets you see the whole flow at once — essential past 8-10 steps.
3. Native iterators and aggregators. On Zapier, processing a list means Code by Zapier or workarounds. On Make, it's native.

My typical setup today: Zapier for 70% of my simple workflows, Make for the 30% that are complex.

Real access conditions I've measured

Across my own usage plus my clients':

• Zapier Professional plan: covers most SMBs up to about 2,000 multi-step executions per month.
• Zapier Team plan: necessary once several people are maintaining the Zaps and you cross 10,000 tasks.
• Make Pro: equivalent to Zapier Team in volume, at a lower net access conditions for complex workflows.

The classic trap I see on assignments: leaving Zaps running in an error loop (a broken API endpoint, for instance) and quietly burning hundreds of useless tasks. I always recommend a monthly audit of consumption per Zap.

GDPR compliance: the real questions to ask

Zapier is a US company, and even though the platform offers localization features, processing transits through their infrastructure. For workflows that touch personal data of European residents:

• Signed DPA in the Zapier console (Article 28 of the GDPR is mandatory).
• GDPR data processing enabled in the settings.
• No sensitive data (health, opinions, etc.) on lower-tier plans without a prior assessment.
• Transfer documentation: since the invalidation of the Privacy Shield and the new transatlantic framework, the obligations are precise. See the CNIL recommendations on transfers outside the EU.

My GDPR checklist for AI tools details the points to validate.

My read for Trust-Vault

Zapier is mature and reliable. The platform runs without notable incidents over time, the documentation is extensive, the access conditions is clear — even if the "1 step = 1 task" mechanic can surprise you at first. The main thing to watch remains compliance when you're handling European personal data.

Make.com targets the same goals with a more technical positioning. For someone comfortable with a rich visual interface who wants fine-grained control over execution access conditions, it's the natural alternative.

To explore complementary automation and productivity tools, see my Productivity catalog.