Firecrawl vs Crawl4AI: Which One Fits Your Stack
Firecrawl vs Crawl4AI, measured the honest way: one run live today, the other priced from its own credit table. Licenses, costs, ops, and the caveats.
On this page
Both tools promise the same thing: hand over a URL, get LLM-ready markdown back. The Firecrawl vs Crawl4AI decision trips people up because the tools take opposite roads to it. Firecrawl is a metered hosted API (with an AGPL server you can self-host); Crawl4AI is a Python library that runs in your own process, and I pip-installed it on this machine today and pointed it at a live site. Here is where each one wins, what each costs past the demo, and who should pick which.
The verdict
Pick Firecrawl when crawling is not your product and you want somebody else’s on-call rotation absorbing the broken pages. Pick Crawl4AI when the crawl is a function inside your own code, the page volume is high, the content is sensitive, or per-page meters make you itch. Everything below is the reasoning behind that split.
| Firecrawl | Crawl4AI | |
|---|---|---|
| What it is | Hosted scraping API; the server code is open (AGPL-3.0) | Python library (v0.9.5, Oct 14, 2026); optional hosted cloud |
| Setup | Sign up, get an API key, one curl | pip install -U crawl4ai + a browser install; about 6 minutes here |
| Price | 1,000 free credits/month, then $19–$83+/month | $0 for code; your CPU, RAM, and bandwidth |
| License | AGPL-3.0 (Sideguide Technologies Inc.) | Apache-2.0 plus a written attribution requirement |
| JS rendering | Managed browser fleet plus Fire-engine, their anti-bot layer | Playwright in your process: Chromium, Firefox, or WebKit |
| Structured extraction | JSON/Question/Highlight formats: +4 credits per page | CSS/XPath/regex schemas free; LLM extraction with your own key |
| Deep crawl | Crawl endpoint, 1 credit per page | BFS/DFS/best-first strategies with resumable state, free |
| Web search | Search endpoint, 2 credits per 10 results | Not in the library; the cloud service sells /search |
| Who owns breakage | Their operations team | You |
| GitHub stars (Oct 16, 2026) | 192,063 | 85,655 |
Firecrawl vs Crawl4AI at the terminal
I ran Crawl4AI’s current release end to end so the setup cost is measured, not imagined. pip install -U crawl4ai into a fresh venv took 5 minutes 28 seconds on this WSL box and left 837 MB of packages behind. The bundled crawl4ai-setup then downloaded its own Chromium into ~/.cache/ms-playwright and failed on the system-dependencies step, because this box has no passwordless sudo. The browser ran anyway since earlier tooling had already installed the OS libraries. On a bare server, budget one apt-get for Playwright’s dependencies before your first crawl.
First scrape, quotes.toscrape.com, cold browser pool:
from crawl4ai import AsyncWebCrawler
import asyncio
async def main():
async with AsyncWebCrawler(verbose=False) as crawler:
r = await crawler.arun(url="https://quotes.toscrape.com/")
print(r.markdown.raw_markdown[:300])
asyncio.run(main())What the first run returns
That returned success in 1.99 seconds including browser startup, with 4,375 characters of raw markdown. One gotcha the README buries: r.markdown.fit_markdown, the boilerplate-stripped version, came back as an empty string until I passed a filter explicitly. With DefaultMarkdownGenerator(content_filter=PruningContentFilter()) configured, the same page yielded 3,663 filtered characters in 1.19 seconds warm, and v0.9.5 printed a deprecation notice pointing to PruningContentFilterLXML, which the project says is about ten times faster.
Deep crawl and extraction
A deep crawl is one more config object. BFSDeepCrawlStrategy(max_depth=1, max_pages=10) over the same site returned 10 of 10 pages in 3.07 seconds, 23,670 characters of markdown total, with Chromium processes peaking at 918 MiB of RSS. That number is the real price of “free”: Crawl4AI bills in RAM.
Structured extraction also costs nothing if you skip the LLM. JsonCssExtractionStrategy with a three-field schema (quote text, author, tags) pulled 10 items off the same page in 1.31 seconds with zero model calls. One honest wart: the tags field, declared as type list, came back missing from every item on v0.9.5 while the plain text fields extracted cleanly. No error, no warning, just absent keys in the JSON. That is exactly the failure mode you sign up to debug when the library is yours.
I did not run Firecrawl, because running it means creating an account, and I don’t create accounts. Its pitch is exactly the opposite trade: no browser in your process, ever. One POST to api.firecrawl.dev/v2/scrape returns markdown, and their README claims 96% web coverage and a P95 latency of 3.4 seconds across millions of pages. Those are their published benchmarks, not mine; what I verified today is the price list that meters every one of those requests.
Two licenses, read literally
Both repos are open source, and both licenses carry a sentence most comparison posts skip.
Firecrawl’s LICENSE file is the verbatim GNU AGPL-3.0, copyright Sideguide Technologies Inc. If you self-host and modify it to power a network service, AGPL obliges you to offer that modified source to its users; internal tools and plain self-hosting never trigger that clause. The hosted product exists so that most people never have to think about any of it.
Crawl4AI ships Apache-2.0 with an appendix titled “Attribution Requirement”. Any distribution, publication, or public use must carry this line, quoted from the LICENSE file:
This product includes software developed by UncleCode (https://x.com/unclecode) as part of the Crawl4AI project (https://github.com/unclecode/crawl4ai).
The appendix names where it goes: NOTICE or README for software distributions, an About or Credits section for websites, help output for CLI tools, acknowledgments for articles. Stock Apache-2.0 has no such clause, so this is an extra condition stitched onto a permissive license. For almost everyone it costs one line of text. But if your shipped product skips that line, you are using the code outside its terms, and that is exactly the sort of thing a compliance review exists to catch.
The bill at real volume
Firecrawl’s pricing page (rates effective September 4, 2026) prices everything in credits. A page costs 1 credit on scrape, crawl, and map. Search costs 2 credits per 10 results. The JSON, Question, and Highlight formats add 4 credits per page on scrape and crawl. A scrape returning an error page like a 403 still costs 1 credit, while one returning nothing is free. The free tier is 1,000 credits per month, and the plan table behind it is modest: 2 concurrent browsers and 2 requests a minute overall. Those 1,000 credits are a weekend-sized budget, not a pipeline feed. Hobby is $19 month-to-month ($16 on annual billing) for 5,000 credits, and Standard is $83 per month billed annually for 100,000.
| Monthly volume | Firecrawl cost | Crawl4AI cost |
|---|---|---|
| 1,000 pages | Free tier, consumed exactly | $0 |
| 5,000 pages | Hobby plan, $19/month | $0 |
| 10,000 pages | Still inside Standard’s 100,000 credits, $83/month | $0 plus RAM: 918 MiB peak for a 10-page crawl here |
| 10,000 pages weekly, JSON extraction on | 5 credits/page x 40,000 pages = 200,000 credits, double the Standard plan; overage lands at $5 per 2,000 credits | $0 plus your LLM key, if you want LLM-powered extraction |
The local costs hide in line items a hosted API never shows you: the 837 MB venv, the 918 MiB of browser RAM, the bandwidth, and the hour you lose when a Chromium update breaks on your CI runner. Whether that trade is cheap depends entirely on your volume. At ten thousand pages a week, running it yourself saves hundreds of dollars a month. At two hundred pages a day, $16 is less than the electricity.
A footnote to all this: Crawl4AI launched a hosted cloud of its own, currently flagged a soft launch on the pricing page, with 1,000 free credits a month and paid plans from $10. If you picked Crawl4AI specifically to avoid meters, ignore that product; if you want Crawl4AI’s markdown without running browsers, it exists now. Deeper math on per-page economics generally lives at LLM scraping cost.
When the page fights back
Friendly sandboxes flatter every crawler. The serious difference between these two shows up on sites that don’t want robots, and here the honest answer is asymmetric.
Firecrawl’s hosted service runs Fire-engine, their in-house layer of rotating proxies, browser stealth, and unblocking machinery. The 96% coverage claim lives there. Their own self-hosting docs are candid that the open-source stack ships “bundled Playwright with basic fetch fallback”, meaning the hard problem is only solved for paying API customers. Self-hosting gets you the API shape, not the armor.
Crawl4AI hands you the raw parts instead: a stealth mode flag, an undetected-browser adapter, proxy configuration with rotation, and an antibot detector module in the package. Whether that is enough depends on the site and on your willingness to buy residential proxies and tune settings. I have not stress-tested either tool against protected sites for this page, so treat both vendors’ claims there as marketing until you try your actual target. What I can say from the run above: on a cooperative site, the local library is not slower in any way you will notice.
Who owns the breakage
Self-hosting Firecrawl, per its SELF_HOST.md, means Docker Compose running the API and workers, a Playwright service, Redis, RabbitMQ, NuQ on PostgreSQL, and optionally FoundationDB. The API publishes on port 3002, authentication defaults to off behind a USE_DB_AUTHENTICATION=false flag, and the compose file defines no persistent volumes, so backups are your project. Their summary sentence is admirably blunt: “Self-hosting gives you source and infrastructure control. You also own security, availability, capacity, upgrades, data retention, and compliance.”
Crawl4AI’s version of this burden is smaller but real: one Python process, no queue, no dashboard, resume state for long crawls, and nothing monitoring itself. Six containers failing is worse than one process failing, but only if somebody is watching either. If you want the managed experience without self-hosting, that is literally Firecrawl’s paid product, and increasingly Crawl4AI Cloud’s pitch too.
A third shape exists between them: a local binary with a GUI, no per-page meter, and watch mode with webhooks. That is Magpie, and it earns its own page at Firecrawl alternatives; there is also a dedicated Crawl4AI alternatives rundown. This comparison stays on the head-to-head.
FAQ
Is Crawl4AI really free?
The library is: Apache-2.0 code, no API key, no meter, and the crawl runs in your own process. The license fee for public use is the one-line attribution. A hosted cloud now exists alongside it (1,000 free credits monthly, plans from $10, marked soft launch), but nothing in the library requires it.
Can Firecrawl be self-hosted for free?
Yes. The repo is AGPL-3.0 and its Compose stack brings up an unauthenticated API on port 3002. What self-hosting does not include: Fire-engine unblocking, screenshots, page actions in the default stack, and any managed infrastructure. You own auth, backups, and upgrades from day one.
Which is better for feeding a RAG pipeline?
Markdown quality is close: Firecrawl’s only-main-content and Crawl4AI’s fit_markdown both aim to cut tokens. Decide on where compute runs. Firecrawl bills per page and sends content through their cloud; Crawl4AI spends your RAM and keeps content in-process. The output-format trade-offs are compared in markdown vs HTML for LLMs.
Does Crawl4AI need an API key?
No. pip install, one import, arun(url). A key enters the picture only when you opt into LLM extraction (any provider, your own key) or sign up for their cloud. For CSS-schema extraction like the structured jobs in LLM extraction vs CSS selectors, no key is needed either.
The decision rule
Crawl one real page from your actual target with each tool before deciding; the free tier covers it on both sides. If Firecrawl’s output holds up on the sites you care about, buy the credits and never think about browsers again. If Crawl4AI’s output holds up, adjust the config object when it doesn’t and keep your $19. The teams that regret this choice are the ones who picked from feature tables instead of testing who fixes the next 3 a.m. failure.
