Why I built Crawlora, after 9 years of rebuilding the same crawler plumbing

I've spent about 9 years building crawlers — starting with a single-machine crawler that could pull a million pages in 2 hours (2017), then distributed crawlers at scale for TikTok and Google Business profiles (2023), then the infra work: what it actually costs to push 10M requests through in 10 minutes.

Every one of those projects started the same way, though: before I could touch the actual problem, I had to rebuild the same plumbing — proxy rotation, headless browser fleets, per-site parsers, retry/fallback logic, and enough cost engineering to keep the whole thing from bankrupting the project. That's the part I got tired of rebuilding.

Crawlora is that decade of crawling-at-scale work productized. Instead of a scraper you own and babysit, you call a documented REST endpoint (or one of 890+ hosted MCP tools) and get normalized JSON back — search/SERP, maps, e-commerce, app stores, social, media, and finance, 920+ endpoints across 84+ platforms. There's also a hosted MCP server (mcp.crawlora.net/mcp) so Claude/Cursor/Cline/n8n agents can call the same tools with one header, no separate integration per platform.

Pricing is pay-on-success — you're billed 1–10 credits only on a 2xx response, 4xx/5xx cost nothing — with a free tier of 2,000 credits/month and no card required. There's a public Playground (crawlora.net/playground) if you want to see the JSON before writing any code.

Still early and still very much a solo/technical build — I'd rather hear where the coverage or docs fall short than oversell what's there. Happy to go deep on the architecture (proxy/headless orchestration, schema normalization, the MCP layer) if anyone's curious.