Scrapingdog vs fastCRW: Legacy Proxy or Modern

By the fastCRW team · Pricing/features verified 2026-05-18 · Verify independently before buying.

Disclosure: We build fastCRW. This is a vendor-authored comparison, so weight it accordingly.

Scrapingdog vs fastCRW at a glance

The short version of Scrapingdog vs fastCRW: they belong to two different generations of web-scraping tools. Scrapingdog is a proxy-rotation API — you point it at a URL, it routes the request through a rotating proxy pool and hands you back the raw HTML (plus dedicated parsers for a handful of high-value targets like Google, LinkedIn, and Amazon). fastCRW is an AI-native engine: it returns clean, LLM-ready markdown or structured JSON, crawls whole sites, and runs web search, all behind a Firecrawl-compatible REST surface you can self-host as a single static Rust binary under AGPL-3.0.

So the real decision isn't "which scraper is faster" — it's "do I need a proxy pool that fetches raw HTML, or an engine that fetches and shapes content for an LLM pipeline?" Most of this post is about that distinction.

Proxy-rotation scraping: Scrapingdog's model

Scrapingdog's core job is getting a successful fetch off a target that fights back. Its rotating proxy pool — residential and datacenter IPs, automatic retries, optional JS rendering — is the product.

The trade-off lives in the output. A proxy API returns the page's raw HTML. That's fine when you have a tuned parser, but for an LLM or RAG pipeline it means you still own the whole cleaning step: strip nav and boilerplate, drop scripts and ads, collapse whitespace, and convert to something a model can ingest without burning tokens on markup. Scrapingdog softens this for its named targets — its Google, LinkedIn, and Amazon endpoints return structured JSON — but for the long tail of arbitrary sites, "scrape" still means "fetch HTML and figure out the rest yourself."

AI-native output: where fastCRW is built differently

fastCRW inverts the default. A single /v1/scrape call returns clean, LLM-ready markdown — boilerplate stripped, content preserved — so the output drops straight into a prompt or a vector store. Need structure instead? Pass formats: ["json"] with a jsonSchema and an LLM extracts exactly the fields you defined (extraction is the 1-credit scrape plus the LLM token cost, billed as usage-metered LLM credits; /v1/extract also accepts up to 50 URLs per request for batched extraction).

Because it's Firecrawl-compatible, migrating off a proxy API or onto fastCRW from a Firecrawl SDK is usually a base-URL swap, not a rewrite. And it goes beyond single-page fetches: /v1/crawl walks a whole site (BFS, maxDepth cap 10, maxPages cap 1000), /v1/map discovers every URL, and /v1/search runs web search with optional inline content scraping — one credit model across all four. A proxy-rotation API gives you the fetch; fastCRW gives you the fetch plus the shaping, the crawl, and the search. For more on the output layer, see LLM-ready markdown extraction.

Where Scrapingdog has a narrow edge

• Pre-built SERP and target parsers. Scrapingdog's dedicated Google, LinkedIn, and Amazon endpoints are turnkey for those specific sources — no schema to write.
• Simple per-request HTML fetching. If all you want is "hand me this page's HTML," a proxy API is a focused, low-ceremony tool.

Where fastCRW wins

• Highest truth-recall of the three tools tested. On Firecrawl's own public scrape-content dataset — 819 labeled URLs, harness diagnose_3way.py, run 2026-05-08 — fastCRW recovered correct content on 63.74% of labeled URLs, ahead of Crawl4AI (59.95%) and Firecrawl (56.04%), with 91.8% scrape-success (of reachable URLs) and 0 thrown errors. Latency note: p50 is 1,914 ms (fastest of the three); in fast mode p90 is 4,348 ms — the lowest of the three (Crawl4AI 4,754 ms, Firecrawl 6,937 ms). Always read the full benchmark split, never a single average.
• Clean output by default. Markdown or schema-driven JSON, not raw HTML you have to post-process.
• Anti-bot and proxy rotation built in. 12-signal block detection, user-agent rotation, stealth fingerprints, and a residential-proxy egress tier ship in the open core and run automatically as part of the render escalation ladder — no separate proxy product to buy.
• Whole-site crawl + map + search in one engine and one credit model.
• Self-host free under AGPL-3.0. The same engine runs on your own box at $0 per 1,000 scrapes (you pay only your server), so data never leaves your infrastructure — something a cloud-only proxy API structurally cannot offer.

Pricing

fastCRW uses one predictable credit model across every operation: scrape costs 1 credit (any renderer, anti-bot handling included), crawl 1 per page, search 1 per query, map 1, and a JSON extraction is that 1-credit scrape plus the LLM token cost, billed as usage-metered LLM credits (scaling with page size and token usage, not a fixed multiple). The free tier is 500 one-time lifetime credits; paid plans start at $13/mo (check live /pricing rather than trusting a number in a blog post). Proxy APIs like Scrapingdog typically meter on requests-with-rendering and proxy type, so the only fair comparison is to model your own request mix on both — we won't quote a competitor multiple.

Which to choose

If your binding constraint is turning the web into LLM-ready content — markdown, JSON, crawls, search — with anti-bot and proxy rotation included, and optionally owning the engine yourself, that's the case fastCRW is built for.

Sources

• fastCRW scrape benchmark: diagnose_3way.py, Firecrawl public scrape-content dataset (819 labeled URLs), run 2026-05-08 — see /benchmarks
• fastCRW repo and pricing: github.com/us/crw · fastcrw.com/pricing
• Scrapingdog docs/pricing: scrapingdog.com/pricing (verify independently)

Related: ScraperAPI alternatives · Anti-bot and proxies overview · Best web scraping APIs · LLM-ready markdown extraction