LinkedIn profiles in Make.com: enrich LinkedIn URLs with the HTTP app

• A Make account. Make's Free plan has routers and filters, 1,000 credits a month, 2 active scenarios and scheduled runs at most every 15 minutes. Make says all customers can use its new HTTP app.
• A Datacircle API key: sign up with your work email and your key is on your dashboard. You get a $5 credit, enough for 2,105 profiles through Up2Data.
• A trigger that gives the profile's URL, like https://www.linkedin.com/in/williamhgates.

In the HTTP module below, set Authentication type to API key, then create a keychain under Credentials:

Make keeps the key in the keychain and adds the Authorization header itself, so you don't type the key in the module's headers. Both HTTP modules use it. To change the key, edit it once under Keys in Make's left sidebar. If your Datacircle key leaks, reset it through our API: the old one stops working.

After your trigger, add HTTP, then Make a request, with these settings:

The module sends the same request as this curl command, which you can run in a terminal to check your key:

curl -X POST "https://api.datacircle.dev/v1/profiles/enrich" \
  -H "Authorization: Token $DATACIRCLE_API_KEY" \
  -H "X-Data-Provider: up2data" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.linkedin.com/in/williamhgates"}'

Return error if HTTP request fails on No lets the scenario run on after a 4xx or 5xx: the module returns a private profile's 422 like a 200, with its status code. On Yes, Make stops the scenario on a 4xx or 5xx, and turns it off once the errors in a row reach its Errors before deactivation setting, or at the first one when an instant trigger, such as a webhook, starts it. Make reads a 429 as a rate limit error, and pauses a scheduled scenario's next run for 20 minutes.

Parse response on Yes turns each field of the answer into an item you can map. Make lists them once the module has run, so run the scenario on one row first. We bill that call like any other. Our API answers within 45 seconds, with a 503 when the provider is slow, so a 60 second Timeout gets that answer.

After the HTTP module, add a Router. Give each route a filter on the HTTP module's status code, and make the last one the fallback route, which takes every answer the other filters don't:

Up2Data takes $1 a day per account (421 profiles), with a shared daily limit for all customers, then answers 429 until 00:00 UTC. HarvestAPI has no daily limit. Fetchin has no daily limit either. A call your balance can't cover answers 402. Add funds, from $5, on your dashboard.

On the Daily limit route, add a second Make a request module with the same keychain. It calls Fetchin, at $1.485 per 1,000 against HarvestAPI's $3.70.

A second Router after it sends each Fetchin answer down its route:

Each route ends with its own module that writes the row back, such as Google Sheets' Update a Row, with your trigger's row in Row number. The Found routes write these fields:

Fetchin returns the profile's industry, not the company's, and no headcount, so the Found (Fetchin) route leaves industry and headcount empty. Fetchin's Not found route writes the status and cost_usd, since Fetchin bills the lookup. The other routes write the status alone. Our LinkedIn Profile API page shows the profile's other fields, such as jobs, schools and skills.

We charge your balance these prices, with no markup, as our pricing page shows:

If 1,000 new rows come in one day and the providers find every profile, you pay us $1.86 at most: $1.00 for 421 through Up2Data and $0.86 for the other 579 through Fetchin, less if all our customers together reach Up2Data's daily limit sooner. Fetchin costs less per profile found but bills a profile it can't find, which Up2Data doesn't. It also gives no company industry or headcount, so the scenario calls Up2Data first.

Make also charges its own credits: 1 for each module run of a non-AI app. A row takes 2: the Up2Data call and the module that writes the row. Past the daily limit it takes 3, with Fetchin's call. The trigger takes 1 for each check.

The same call in n8n: LinkedIn profiles in n8n. In a Zap: LinkedIn profiles in Zapier. In a Google Sheet, without Make: LinkedIn profiles in Google Sheets. From Python: get LinkedIn profile data with Python. Other vendors' prices per 1,000: LinkedIn profile API pricing compared.