By the fastCRW team · Pricing/features verified 2026-05-18 · fastCRW launch pricing expires 2026-06-01 · Verify independently before buying.
Disclosure: We build fastCRW. This is a vendor-authored comparison, so weight it accordingly — but we've kept the section on where Scrapingdog genuinely wins explicit, because a comparison that pretends the competitor has none isn't useful to you.
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.
| Dimension | Scrapingdog | fastCRW |
|---|---|---|
| Category | Proxy-rotation scraping API | Open-core Rust engine + managed cloud |
| Default output | Raw HTML (parse it yourself) | Clean markdown / JSON-schema extraction |
| Proxy / anti-bot | Rotating residential + datacenter pool | No built-in Fire-engine anti-bot |
| Crawl & map | Per-URL fetch; no native crawl job | /v1/crawl + /v1/map |
| Web search | SERP parsers (Google/Bing) | /v1/search with optional content scrape |
| Self-host | Cloud-only | AGPL-3.0, single ~8 MB binary, one container |
| API style | Proprietary | Firecrawl-compatible (drop-in base-URL swap) |
Proxy-first scraping: where Scrapingdog leads
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. If your blocker is "this site keeps returning 403 / a CAPTCHA / a bot wall," a mature proxy network is exactly the right tool, and we won't pretend fastCRW has one.
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 retrieval-augmented generation 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 a 5-credit operation, single-URL, and runs on OpenAI or Anthropic providers — stated plainly so there are no surprises).
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 genuinely wins
An honest comparison has to name these:
- Proxy rotation for blocked targets. A managed residential/datacenter pool with retries is real infrastructure. fastCRW has no built-in Fire-engine anti-bot and no residential proxy depth — on heavily defended sites, Scrapingdog (or a dedicated proxy provider) is the better fetch layer. See anti-bot and proxies for the landscape.
- 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 through a clean IP," 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.
- 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 and honesty
fastCRW uses one predictable credit model across every operation: scrape costs 1 credit (2 with the chrome renderer), crawl 1 per page, search 1 per query, map 1, and JSON extraction 5. The see plan pricing is 500 one-time lifetime credits; paid plans start at $13/mo launch pricing (reverts to regular on 2026-06-01 — 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.
The honesty line we'll repeat: fastCRW has no built-in residential proxy pool or anti-bot engine. If your targets actively block scrapers, you'll either pair fastCRW with a proxy layer or pick a proxy-first tool like Scrapingdog. We'd rather you know that up front than discover it on a hostile site. For the broader proxy-tool field, see our ScraperAPI alternatives roundup, which covers the same legacy-proxy generation.
Which to choose
| You are… | Pick |
|---|---|
| Fetching heavily defended sites that need proxy rotation | Scrapingdog |
| Pulling Google / LinkedIn / Amazon via a turnkey parser | Scrapingdog |
| Feeding an LLM / RAG pipeline that wants clean markdown | fastCRW |
| Extracting structured JSON against your own schema | fastCRW |
| Crawling whole sites or running web search in one engine | fastCRW |
| Needing self-host so data never leaves your infra | fastCRW |
If your binding constraint is getting past a bot wall, lead with a proxy. If your binding constraint is turning the web into LLM-ready content — markdown, JSON, crawls, search — and optionally owning the engine yourself, that's the case fastCRW is built for. The two even compose: a proxy layer in front of fastCRW gives you both the fetch reliability and the AI-native output, with the option to self-host the shaping engine for free.
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