Zapier vs Make for AI workflows

Last month I needed to pull fresh product descriptions from a Google Sheet, run them through an AI model, and push the results back to a Shopify store—all without writing code. I tried both Zapier and Make to see which could handle the AI step reliably. Here’s what happened.

What most guides get wrong

Most tutorials treat Zapier and Make as interchangeable glue layers and then slap an AI module on top. They ignore the fact that the AI call itself often becomes the bottleneck, not the workflow orchestrator. In my test, the difference wasn’t in the number of steps but in how each platform handles retries, rate limits, and data shaping before the AI sees the input.

Guides also tend to praise the visual builder without mentioning that Make’s scenario execution log can become a wall of text when you nest routers, while Zapier’s task history hides the raw payload unless you dig into the “Input Data” tab. Both omissions lead to wasted debugging time.

How Zapier handles AI steps

First, I added a **Zapier** trigger: New Spreadsheet Row in Google Sheets. Then I used a Webhooks action to call the OpenAI API directly because Zapier’s built‑in OpenAI action only supports the older Davinci model and forces you into a fixed token limit.

Here’s the prompt I sent, exactly as it appears in the Webhooks body:

{
  "model": "gpt-4o",
  "messages": [
    {
      "role": "system",
      "content": "You are a copywriter that creates short, punchy product descriptions."
    },
    {
      "role": "user",
      "content": "Summarize the following product description in two sentences: {{description}}"
    }
  ],
  "temperature": 0.7
}

Zapier automatically URL‑encodes the JSON, so I had to set the “Headers” to Content-Type: application/json and tick “Unflatten” to keep the nested structure. The response came back as a raw text block that I then parsed with a Utilities > Text action to extract the AI output.

What I liked: the built‑in retry policy (up to three attempts with exponential backoff) saved me when OpenAI returned a 429 rate‑limit error. What I disliked: Zapier charges each Webhooks call as a separate task, so a single AI interaction consumed two tasks (request + response parsing). On the Professional plan at $49/mo you get 2,000 tasks, which means roughly 1,000 AI calls before you hit the limit.

Concrete gripe: The free plan’s 100‑task monthly cap is invisible until you hit it; I got an email saying my Zap was turned off mid‑day, with no warning in the UI.

How Make handles AI steps

I rebuilt the same flow in **Make**. The Google Sheets module watches for changes, then I added an HTTP > Make a request module pointed at https://api.openai.com/v1/chat/completions. I pasted the same JSON body, but Make lets you map variables directly into the JSON tree without manual string‑building.

Make’s HTTP module automatically handles authentication headers if you set up an OpenAI connection, which spared me from copying the API key into every scenario. The response is returned as a bundled collection that you can parse with a JSON > Parse module or, even simpler, map the “choices[0].message.content” field straight into the next module.

What I loved: Make’s router lets me split the flow based on the AI output length—if the summary is under 20 characters I send it to a Slack alert for manual review, otherwise it goes straight to Shopify. This branching happens without adding extra modules; you just drag a router onto the canvas and set conditions.

What I disliked: The execution log shows each operation as a separate line, and when you have nested routers the log can swell to dozens of entries for a single run, making it hard to locate the exact HTTP call that failed. You need to click through each operation to see the raw request and response.

Concrete love: The ability to reuse a single OpenAI connection across multiple scenarios means I only store the API key once, and if I rotate it I update it in one place.

Why does my AI workflow break at 500 runs?

After about five hundred successful runs I started seeing intermittent 502 errors from the OpenAI endpoint, but only in Zapier. The pattern was: the first retry succeeded, the second attempt failed with a timeout, and the third attempt was never made because Zapier had already marked the task as errored.

In Make the same scenario kept retrying until the HTTP module’s max attempts (default three) were exhausted, then it moved to the error handling route I’d defined. The difference lies in how each platform treats a partial success: Zapier treats any non‑2xx response as a final failure unless you enable “Auto‑retry on failure” *and* set the retry count manually, while Make’s HTTP module retries on network errors by default and lets you customize the condition.

Root cause: OpenAI occasionally returns a 502 when their backend is overloaded. Zapier’s default retry only covers connection timeouts, not HTTP 5xx, unless you dig into the advanced settings.

How to debug when this breaks

First, check the task history. In Zapier open the failed task, click “View Details”, then look at the “Input Data” and “Output Data” tabs. If the output is empty, the AI call didn’t return a body—most likely a network issue.

In Make, go to the scenario log, find the red HTTP module, and click the “i” icon to see the raw request and response. Look for the status code and any error message in the body.

Second, add a simple error‑catcher. In Zapier add a Filter step after the Webhooks action that only continues if the response contains a “text” field; otherwise route to an Email by Zapier alert. In Make attach a router after the HTTP module: one branch for status code 200, another for anything else, and connect the latter to a Slack webhook or email.

Third, monitor your token usage. Both platforms will happily send the same prompt repeatedly if the AI returns an error and you don’t stop the loop. I added a counter variable that increments each time the AI module runs; if it exceeds five attempts the scenario halts and flags a manual review.

One‑sentence paragraph: It’s frustrating when the platform hides the exact HTTP status behind a generic “Task failed” message.

Pricing and my honest take

Zapier’s Professional plan at $49/mo gives you 2,000 tasks, which translates to roughly 1,000 AI calls if you use the Webhooks‑plus‑parser method I described. Make’s Core plan at $29/mo provides 10,000 operations, and each HTTP request counts as one operation, so you get about five times more AI calls for less money.

I think Zapier’s AI built‑in actions are overpriced for what they do—paying extra for a wrapper that only supports outdated models feels like a waste when you can call the API directly for the same cost.

Make’s free tier is enough for solo work: you get 1,000 operations a month, which covers about 200 AI runs, perfect for testing a new product‑description pipeline before you commit to a paid plan.

Adjacent reading: deeper coverage of AI agent platforms.

If you’d rather skip the build and deploy a working version in an afternoon, we’ve packaged this workflow as a blueprint at deepusecase.com/vault.