RvanyData

Tried scraping Google Maps with Selenium.

Worked for 1 day. Spent the next 2 weeks fixing CAPTCHAs.

I’ve started thinking of Google’s SERP as a dataset.

Not a destination.

That mental shift alone changed how I do SEO.

Most SEO tools summarize search.

SERP APIs expose search.

Big difference.

One gives metrics.

The other shows how Google actually structures opportunity.

That’s where the interesting stuff is.

Google Shopping SERPs show what the market actually looks like.

That gap matters.

Especially if you care about:

price monitoring competitor tracking product visibility ad placement marketplace intelligence

The feed is the input.

I used to think Google Shopping tracking was just “search product, copy price, repeat.”

Then I tried doing it across locations.

Everything changed.

Same keyword. Different city. Different sellers. Different prices. Different sponsored placements.

That is when Google Shopping stopped looking like a search page and started looking like a live market signal.

Hot take:

Using proxy-heavy stacks for search-specific data may be becoming legacy architecture.

After testing TalorData vs Oxylabs…

I tested TalorData vs Oxylabs…

Honestly? They solve different problems.

One feels built for scraping.

The other feels built for AI-era search infrastructure.

More IPs didn’t reduce blocks.

Cleaner IPs did.

They weren’t.

They passed tests and failed under real traffic.

Biggest lesson:

Proxy quality matters more than giant IP pools.

They use them for:

data collection that should hit source sites

query workloads where economics break

problems that are actually proxy/infrastructure problems

I learned this the expensive way.

SERP APIs are great for leverage. Terrible as a universal default.

Sometimes scraping Google is the detour. Not the shortcut.

They struggle because the search data underneath is messy.

Most people think scraping Google is a proxy problem. It’s not. It’s a behavior problem.