Apify Google Ads Scraper Alternative, Adyntel

What Apify's Google Ads actor does well

The leading Apify Google Ads actor has over 5.2 million runs and a 4.7-star rating, the highest adoption of any Google ad scraping tool on the platform. It covers Google Ads Transparency Center data, returns ad copy, creative details, and advertiser information, and is maintained by the same developer who built the leading LinkedIn actor (with Clay workflow tutorials included). For teams focused on Google Search ad intelligence and willing to invest in actor configuration, it’s a solid option.

Where it falls short for GTM teams

$30/month before you run a single query. The leading Google Ads actor requires a $30/month base subscription before any compute units. If your enrichment workflow doesn't run every month, you're paying for idle capacity. And that $30 covers only Google. LinkedIn and Meta are separate actors with separate fees stacked on top.

CAPTCHA issues make reliability inconsistent. Google's Ads Transparency Center implements anti-automation measures including CAPTCHAs. Community feedback consistently rates Google ad scraping as "hit or miss": some runs succeed, others fail without useful error messages. In a production enrichment workflow, unpredictable failures break automation.

Keyword or advertiser name input. Google Ads actors typically take a keyword or advertiser name as input. Searching by company name introduces ambiguity (which "Acme" among multiple results is your target?), and doesn't slot cleanly into a domain-first enrichment pipeline.

Still just one platform. Even after paying the $30 base fee and solving CAPTCHA reliability, you have Google data only. Most GTM enrichment workflows that care about Google also care about LinkedIn and Meta. Adding those is three separate actors, three billing models, and three output formats to reconcile.

Side by side

Feature	Apify Google Ads actor	Adyntel
Input format	Transparency Center advertiser URL (you find the advertiser, then copy the URL)	Company domain (company.com); we resolve to the right advertiser
CAPTCHA reliability	"Hit or miss" (community-rated)	99.99% uptime, dedicated infrastructure
LLM / AI	No MCP; output format varies by actor	One JSON schema, LLM-ready; MCP server for Claude, Cursor, and other clients
Platform coverage	Google only (LinkedIn + Meta = separate actors)	Google + LinkedIn + Meta
Total cost (all 3 platforms)	$30+ base (Google) + compute × 3 actors	$44/month, all platforms included
Failed run cost	Compute units used regardless of result	No charge for failed or empty lookups

What really stands out

Domain in, we resolve. Adyntel’s Google API takes a company domain (company.com); we map it to the correct Google advertiser and return every active ad. Apify’s scraper needs a Google Ads Transparency Center advertiser URL, so you search or browse to find the advertiser first, then pass that URL in. No domain-first option.
LLM-ready, with MCP. Adyntel returns one consistent schema; Claude, Cursor, and other MCP clients can query it directly. Apify offers multiple export formats (JSON, Excel, CSV, etc.) and mentions Clay/ChatGPT/n8n, but there’s no MCP server and output shape varies by actor.
Channel mix in one place. One account and one credit pool for Google, LinkedIn, and Meta. One call per platform, same domain input; no wiring separate actors or reconciling different schemas.

Who each tool is best for

Use Apify Google Ads actor if...

You specifically need Google-only data at high volume
You're building a custom scraping workflow and want full control
You search by keyword or category rather than by specific company domain
You're comfortable handling CAPTCHA failures in your pipeline

Use Adyntel if... ← probably you

One endpoint, domain in, consistent JSON
Google + LinkedIn + Meta without stitching; pay only when ads are found
Structured for LLMs; query from Claude or Cursor via our MCP server

"When prospects saw we actually knew their ad mix, they started replying in ultra-relevant manners, referencing the exact ads they had been running and getting traction on vs those that they wanted to make the switch to video for."