Web Scraping APIs

Crawl4AI vs Firecrawl: Which to Pick

By the Scrappey Research Team

Crawl4AI vs Firecrawl: Which to Pick — conceptual illustration
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Crawl4AI and Firecrawl both turn a URL into clean Markdown for LLMs, but they sit on opposite ends of the build-vs-buy line: Crawl4AI is a free, self-hosted Python library under Apache 2.0, while Firecrawl is a managed-cloud API under an AGPL-3.0 server with a free tier and paid plans. Crawl4AI runs on your own machines with your own LLM keys (including a local Ollama model), so there is no per-scrape charge but you own the browsers, proxies, and uptime. Firecrawl runs the browser fleet, anti-bot handling, and proxies for you behind a single API call, plus an MCP (Model Context Protocol, a standard way AI tools call external services) server and the FIRE-1 agent for multi-step sites. Pick Crawl4AI for control and cost; pick Firecrawl for the fastest time to results without maintaining infrastructure.

Quick facts

Hosting modelCrawl4AI: self-hosted only. Firecrawl: managed cloud (also self-hostable)
LicenseCrawl4AI: Apache 2.0. Firecrawl: AGPL-3.0 server, MIT SDKs/UI
CostCrawl4AI: free (you pay infra). Firecrawl: free tier, paid from ~$16/mo
LLM / OllamaCrawl4AI: BYO via LiteLLM incl Ollama. Firecrawl: managed, no local LLM
Default outputBoth: clean Markdown (plus JSON/HTML)

Hosting, license, and cost: the core split

The biggest decision is who runs the browsers. Crawl4AI is a Python library you install and operate yourself - there is no public managed cloud (their hosted API has stayed in closed beta), so self-hosting is the path. You import AsyncWebCrawler, point it at a URL, and it runs Playwright on your hardware. That means no per-scrape bill, but the real cost is the servers running the browsers, any proxy service, your LLM keys, and the DevOps time to keep it healthy.

Firecrawl flips this: you call an HTTP API and Firecrawl runs the browser fleet, proxies, and retries. Its free tier is around 1,000 credits per month (roughly one credit per standard page scrape) with no card required, and paid plans start near $16/month on the Hobby tier when billed yearly, scaling up by volume. Note that the FIRE-1 agent is billed even on failed runs, so account for that in agent-heavy workloads.

Licensing differs too. Crawl4AI is Apache 2.0 - permissive, with an explicit patent grant - so you can embed it freely. Firecrawl's server is AGPL-3.0 (its SDKs and UI components are MIT); AGPL means if you offer a modified Firecrawl server as a network service you must publish your source changes, which matters if you plan to self-host and resell it.

Anti-bot, LLM choice, and the MCP/agent story

Anti-bot handling is where the managed model earns its fee. Crawl4AI ships a stealth mode (it can set BrowserConfig(enable_stealth=True) and uses playwright-stealth to adjust fingerprints) and an undetected-browser adapter aimed at tougher targets, plus proxy rotation you configure. But because it drives a real browser over CDP (the Chrome DevTools Protocol that automation tools use), it inherits the same deep-fingerprinting walls as any Playwright setup on the hardest sites, and you supply and rotate the proxies. Firecrawl runs its own managed browser-and-proxy fleet, so anti-bot work and IP rotation happen server-side without you configuring anything.

LLM flexibility favors Crawl4AI. Through LiteLLM it can call OpenAI, Anthropic, Gemini, or a local model via Ollama or vLLM, so extraction can run entirely on your own machine with no data leaving your network - useful for privacy or cost. Firecrawl's extraction is part of its managed service; you do not plug in a local Ollama model.

On the AI-agent front, Firecrawl is further along. It ships an official MCP server so Claude Code, Cursor, or any MCP client can call firecrawl.scrape, firecrawl.search, and firecrawl.crawl in plain language, and the FIRE-1 agent handles multi-step navigation such as forms, searches, and pagination on its own. Crawl4AI is a library you wire into your own agent code rather than a hosted MCP endpoint.

When to pick each

Choose Crawl4AI when control, privacy, or per-scrape cost dominate. If you want data and LLM inference to stay in-house (local Ollama), you are comfortable running Playwright and proxies, and your targets are not the most heavily defended, the free Apache-2.0 library is hard to beat. It is also a good fit for RAG ingestion pipelines where you want adaptive crawling - a built-in rule that stops once new pages stop adding information - and clean Markdown tuned for fewer tokens.

Choose Firecrawl when time-to-results and hands-off anti-bot matter more than control. If you want a single API call that returns Markdown or JSON, a managed proxy fleet, an MCP server for agents, and FIRE-1 for multi-step interaction - and you would rather pay than operate infrastructure - Firecrawl gets you there faster. Both genuinely win on different axes: Crawl4AI on cost, openness, and local-LLM support; Firecrawl on managed anti-bot, agent tooling, and zero ops.

A third pattern is common in production: self-host Crawl4AI for the bulk of easy pages to keep costs low, and route only the hardest, well-defended URLs to a managed web-data API that folds proxies, a real browser, and retries into one call. That keeps your spend on the small slice of traffic that actually needs it.

Code example

python
# Same task, two tools: URL -> typed product data.
# Crawl4AI runs on your box with a local Ollama model (no API cost).
# Firecrawl runs in the cloud and returns Markdown you then parse.

import asyncio

# --- Option A: Crawl4AI (self-hosted, local LLM) ---
from crawl4ai import AsyncWebCrawler, LLMExtractionStrategy
from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price_usd: float
    in_stock: bool

async def with_crawl4ai(url: str):
    strategy = LLMExtractionStrategy(
        provider="ollama/llama3.3",          # local model, nothing leaves your network
        schema=Product.model_json_schema(),
        instruction="Extract the product details from this page.",
    )
    async with AsyncWebCrawler() as crawler:   # Playwright runs on your hardware
        result = await crawler.arun(url=url, extraction_strategy=strategy)
        return result.extracted_content

# --- Option B: Firecrawl (managed cloud, proxies + anti-bot handled) ---
from firecrawl import FirecrawlApp

def with_firecrawl(url: str):
    app = FirecrawlApp(api_key="fc-...")        # cloud runs the browser fleet
    doc = app.scrape_url(url, params={"formats": ["markdown"]})
    return doc["markdown"]                      # clean Markdown, ready for an LLM

if __name__ == "__main__":
    target = "https://store.example.com/product/123"
    print(asyncio.run(with_crawl4ai(target)))
    print(with_firecrawl(target))

Related terms

What Is Crawl4AI?
Crawl4AI is the most-starred open-source LLM-friendly web crawler on GitHub — 60K+ stars under Apache 2.0 license, maintained by UncleCode. …
What Is Firecrawl?
Firecrawl is a web-scraping API built for AI: you hand it a URL and it hands back clean Markdown or JSON — no CSS selectors, no XPath, no HT…
What Is AI Web Scraping?
AI web scraping is an approach that replaces CSS selectors with natural-language prompts, LLM-based extraction, and Markdown-first output. N…
What Is a Web Scraping API?
A web scraping API is a hosted HTTP service that visits a web page for you and hands back the result — rendered HTML, JSON, or already-parse…
Web Scraping Tools 2026 — A Comparison
"Web scraping tools" is the whole family of software you use to pull data off websites — and in 2026 that family is big but neatly sorted in…

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Frequently asked questions

Is Crawl4AI or Firecrawl cheaper?

Crawl4AI is free to use because it is an open-source Apache-2.0 library, but you pay indirectly for the servers running the browsers, any proxy service, your own LLM keys, and the time to maintain it all. Firecrawl charges per scrape after a free tier of roughly 1,000 credits per month, with paid plans starting around $16/month. For low volumes or local-LLM pipelines Crawl4AI usually costs less; at scale the comparison depends on how much infrastructure you would otherwise have to run yourself.

Do both return Markdown?

Yes. Markdown is the default output for both tools, which is why they are so often compared. Markdown uses far fewer tokens than raw HTML, so it is well suited to feeding into LLMs for RAG pipelines. Both can also return JSON when you supply a schema describing the fields you want, and Firecrawl additionally offers HTML and screenshot formats.

Can either one use a local LLM like Ollama?

Crawl4AI can. Through LiteLLM it routes to any provider you name, including ollama/llama3.3 and other local or self-hosted models, so extraction can run entirely on your own machine with no data leaving your network. Firecrawl's extraction is part of its managed cloud service, so you do not plug in a local Ollama model the same way - that is a deliberate trade of flexibility for a hands-off managed experience.

Which has better support for AI agents and MCP?

Firecrawl is further along here. It ships an official MCP server so clients like Claude Code and Cursor can call scrape, search, and crawl tools in plain language, and its FIRE-1 agent navigates multi-step workflows such as forms and pagination on its own. Crawl4AI is a Python library you wire into your own agent code rather than a hosted MCP endpoint, which gives you more control but more to build.

Last updated: 2026-06-16 · Facts last verified: 2026-06-16