Apify Ad Scraper Alternative, Adyntel

What Apify does well

Apify is a serious scraping infrastructure platform with a large community-maintained actor library. For ad data specifically, their actors cover all three major platforms (LinkedIn, Meta, and Google) and the leading actors have thousands of active users. If you're a developer who wants full control over a scraping workflow or needs to scrape things beyond ad libraries, Apify is a legitimate option. The platform is flexible and well-documented.

Where it falls short for GTM teams

No unified domain-based lookup. Every actor on Apify requires a platform-specific input: a LinkedIn Ad Library URL, a Facebook page URL, or a keyword search for Google. You have company domains in your CRM. Mapping those domains to platform-specific IDs is a separate enrichment step that adds friction and fails at scale.

Three actors, three datasets, three bills. To get LinkedIn + Meta + Google data, you're running three separate actors with three different configuration schemas, three separate outputs to parse, and potentially three separate pricing tiers. There's no "get all ad data for this company" call. You stitch it yourself.

Community-maintained means variable quality. Apify actors are built and maintained by community contributors. When LinkedIn updates their Ad Library structure or Meta tweaks their API, actors can break and stay broken until someone in the community pushes a fix. Adyntel maintains its own scraping infrastructure with a dedicated team.

Google alone costs $30/month before you run a single query. The leading Google Ads actor requires a $30/month base fee before compute units. That's just one platform. If you also need LinkedIn and Meta, you're stacking fees for a setup that still requires custom domain resolution and output stitching.

Side by side

Feature	Apify actors	Adyntel
Input format	Platform-specific URL or advertiser ID	Company domain (company.com)
Platform coverage	3 separate actors, separate configs and outputs	LinkedIn + Meta + Google (one API, one call per platform)
Pricing model	Compute units + per-actor base fees	1 credit per successful API call
Maintenance	Community-maintained, variable response time	Dedicated infrastructure team
Uptime	"Hit or miss" (community sentiment)	99.99%

The core difference: input and workflow

Apify actors scrape ad library pages. Each one wants a page-level input: LinkedIn Ad Library URL, Facebook page URL, or a Google keyword. You run three actors (one per platform), get three separate outputs, and stitch them yourself. If your starting point is already a platform URL, that's workable.

GTM teams usually start with domains. A Clay table has 500 or 50,000 rows with acme.com, competitor.io. To use Apify you first resolve each domain to a LinkedIn company ID, a Facebook page URL, and a Google advertiser name. Three enrichment steps before the first actor. Then three actor runs and manual stitching. At 500 companies that's a lot of plumbing. At 50,000 it doesn't hold up.

Adyntel takes the domain. Pass company.com and get LinkedIn, Meta, and Google from one API. One call per platform, same domain input, consistent JSON. No resolution step, no stitching, no per-platform config. Failed or empty lookups don't use credits. The output is structured so you can pass it straight into an LLM; the model can read and use the fields without extra parsing.