Make vs Integromat for AI workflows

Last month I needed to turn a fresh RSS feed into LinkedIn posts using GPT‑4o, then push each piece through a Make scenario. I had used Integromat years ago, so I wanted to see if the rebranded Make felt any different for AI‑heavy work. What follows is a straight‑up walkthrough of what works, what trips you up, and where the price actually makes sense.

Make vs Integromat for AI workflows

Both platforms let you string together modules without writing code, but the devil is in the details when you start calling large language models. Integromat’s old interface still exists for legacy users, while Make’s newer UI tries to be more visual. I found that the core execution engine is identical, yet small UI quirks change how fast you can iterate.

Here’s the scenario I built: an RSS trigger pulls the latest blog entry, sends the title and excerpt to OpenAI’s Chat Completion endpoint with a custom prompt, waits for the generated LinkedIn copy, then posts it via the LinkedIn API module. I ran the same flow on both platforms to compare.

What most guides get wrong

Most tutorials tell you to just drop an HTTP module, add your API key, and call it a day. They skip the part where you need to handle token limits and retry logic. If you blindly send a 4,000‑token prompt to GPT‑4o, you’ll get a 400 error and the scenario stops. The guide also pretends that the free tier gives you enough operations for realistic AI usage, which it doesn’t.

What you actually need is a small JavaScript module (or Make’s built‑in “Tools” > “Set variable”) to count tokens before the call, and a router that sends the request to a fallback model if the token count exceeds a safe threshold. I’ll show the exact prompt I used later.

Concrete named example

Below is the prompt I fed to GPT‑4o to turn a blog snippet into a LinkedIn post. I keep it under 800 tokens to stay safe.

You are a LinkedIn copywriter. Given the blog title "{title}" and the following excerpt:

{excerpt}

Write a concise LinkedIn post (max 150 characters) that highlights the key takeaway, ends with a call to action to read the full article, and uses a professional yet engaging tone. Do not include hashtags.

I stored the title and excerpt from the RSS trigger in two variables, then used the “Set variable” module to assemble the prompt string. The HTTP module calls https://api.openai.com/v1/chat/completions with POST, JSON body containing model “gpt-4o”, messages array with the system and user parts, temperature 0.7, and max_tokens 200.

On Make, the HTTP module lets you click “Test” and see a sample response instantly. On Integromat, the same test button exists but the UI hides the response behind a modal that you have to close to continue editing — a tiny friction point that adds up when you’re tweaking prompts.

How do you handle API rate limits when chaining AI models?

Rate limits are the silent killer of AI workflows. OpenAI imposes 3,500 requests per minute for gpt-4o on a paid tier, but if you burst a scenario that processes ten RSS items at once you can hit the limit quickly. Both Make and Integromat let you add a “Sleep” module, but the default sleep time is hard‑coded to seconds, not milliseconds, which makes fine‑grained throttling awkward.

I solved it by adding a router after the HTTP call: if the response status is 429, the flow goes to a Sleep module set to 2 seconds, then retries the same HTTP module up to three times. If it still fails, the scenario logs an error to a Google Sheet and stops. This pattern works identically on both platforms, but Make’s error handling UI shows the retry count directly on the module, while Integromat forces you to open a separate “Error handling” tab.

Concrete gripe

I hate that Make’s free plan only gives you 1,000 operations a month. A single AI call that includes an HTTP request, a JSON parse, and a variable set already counts as three operations. After processing just ten blog items I was out of ops for the day. The free tier feels more like a trial than a usable plan for any real AI workload.

Concrete love

I love how Make’s scenario editor lets you drag a HTTP module onto the canvas and instantly test the request with a sample payload. The response appears in a side pane without leaving the editor, which cuts the feedback loop from minutes to seconds. When I was iterating on the LinkedIn prompt, that immediacy kept me in flow.

Price mention with opinion

The Core plan at $9 per month gives you 10,000 operations, which comfortably covers a modest AI workflow of a few hundred calls each month. For a solo operator that’s fair. The Pro plan at $29 per month jumps to 100,000 operations — honestly, unless you’re running a batch job that processes thousands of AI calls daily, that tier feels overpriced for what you get.

How to debug when this breaks

When your scenario stops, the first place to look is the execution history. Both platforms show a list of runs with status icons. Click a run and you’ll see each module’s input and output. If the HTTP module returned a 400, open the output tab to see the exact error message from OpenAI — often it’s “max_tokens must be less than or equal to the model’s context length.”

If the error is vague, add a “Set variable” module right before the HTTP call that dumps the full JSON payload into a text field, then run the scenario again. Seeing the raw request helps you spot missing quotes or malformed variables. I’ve found that 80% of my AI workflow bugs are just bad JSON, not the API itself.

Another useful trick: enable the “Enable data loss” option on the HTTP module (Make calls it “Continue on error”) so the scenario doesn’t halt on a single failed call. Then route the error output to a Google Sheet or email notification. That way you keep the rest of the pipeline alive while you investigate the offending item.

Finally, watch your operation count. If you see the scenario stop mid‑batch with no error, you’ve likely hit the plan limit. Upgrade or add a filter that skips items after a certain count to avoid nasty surprises.

We cover this in more depth elsewhere — 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.