Firecrawl vs. Crawl4AI vs. Spider: The Honest Benchmark

Every AI engineer building a RAG pipeline, an autonomous agent, or a training data workflow eventually lands on the same question: which scraping tool should I actually use? Three names keep coming up in every Discord thread, Hacker News post, and “awesome-llm” list: Firecrawl, Crawl4AI, and Spider.

All three are open source. All three target the AI/LLM use case explicitly. All three promise to turn messy web pages into clean data your models can consume. But the marketing pages won’t tell you how they perform when you throw real workloads at them.

We ran a benchmark from our infrastructure. Same URLs, same hardware, same network, same measurement methodology. We are upfront: Spider is our product, and we designed this test. The methodology is described below so you can reproduce it and verify the results on your own workloads.

Benchmark methodology

Getting apples-to-apples numbers from three architecturally different tools takes discipline. We designed the test to eliminate as many confounding variables as possible.

The URL corpus

We assembled a set of 1,000 URLs split across three categories:

URLs were shuffled randomly. Every tool received the identical list in the identical order.

Hardware and network

All three tools ran on the same machine: an AWS c6i.4xlarge (16 vCPU, 32 GB RAM) in us-east-2, connected to a 10 Gbps network. For Firecrawl and Spider, we used their respective cloud APIs from the same instance to measure real-world latency, including network round trips. For Crawl4AI, which has no managed cloud service, we ran the self-hosted Python process directly on the instance.

Each tool was given the same residential proxy endpoint for the anti-bot protected tier, so proxy quality wouldn’t skew the results.

What we measured

Five metrics, each chosen because it maps to a real engineering concern:

1. Pages/second throughput: total pages returned divided by wall-clock time, including retries
2. Success rate: percentage of URLs that returned usable content (HTTP 200 with a non-empty body)
3. Cost per 1,000 pages: actual dollars spent on the cloud API (Firecrawl and Spider) or estimated compute cost (Crawl4AI self-hosted)
4. Markdown quality: measured by downstream RAG retrieval accuracy (more on this below)
5. Time to first result: how long until the first page of content is available in your application

For markdown quality, we fed each tool’s output into the same embedding pipeline (OpenAI text-embedding-3-small) and the same vector store (Qdrant). We then ran 200 factual questions against each corpus and measured recall@5, the percentage of questions where the correct answer appeared in the top 5 retrieved chunks. This tells you how much your retrieval quality depends on which scraper produced the markdown.

The tools

Before the numbers, a brief overview of what each tool is and how it works. This context matters because architectural choices drive the performance differences.

Firecrawl

Language: TypeScript/Node.js License: AGPL-3.0 GitHub: mendableai/firecrawl Cloud: firecrawl.dev

Firecrawl is a TypeScript-based scraping API built by the Mendable team. It focuses on turning web pages into LLM-ready markdown and supports crawling (follow links), scraping (single page), and map (site discovery). The cloud service handles proxy rotation and browser rendering. The open source version requires you to bring your own infrastructure.

Cloud pricing starts at $16/month for 500 credits (pages), scaling to $500/month for 500,000 credits. Overage is billed at roughly $0.003 per page on higher tiers.

Crawl4AI

Language: Python (asyncio) License: Apache 2.0 GitHub: unclecode/crawl4ai Cloud: None (self-hosted only)

Crawl4AI is a Python async crawling framework built for AI data pipelines. It uses Playwright under the hood for browser rendering and provides markdown conversion, chunking strategies, and extraction helpers. It is completely free, with no cloud service and no usage-based pricing.

The trade-off: you run everything yourself. That means provisioning the browser instances, managing proxies, handling retries, and scaling horizontally when throughput matters.

Spider

Language: Rust License: MIT GitHub: spider-rs/spider Cloud: spider.cloud

Spider’s core engine is written in Rust. The cloud API handles proxy rotation, anti-bot bypass, browser rendering, and markdown conversion. The open source crate (MIT licensed) runs standalone if you prefer self-hosting. Cloud pricing is pay-as-you-go with no subscription.

Results

Throughput (pages per second)

This is the headline number. How fast can each tool move through a large URL list?

Spider’s Rust engine processes static pages at nearly 7x the throughput of Firecrawl and 9.5x Crawl4AI. The gap narrows on JS-heavy pages (where the browser is the bottleneck, not the framework), but Spider still leads by 3-4x because its smart mode avoids launching Chrome for pages that don’t need it.

On anti-bot protected pages, all three tools slow down. Spider’s built-in bypass handles Cloudflare and Akamai natively without extra configuration. Firecrawl requires the cloud tier for reliable bypass. Crawl4AI leaves anti-bot handling to the user.

Success rate

The anti-bot tier is where the gap is starkest. Spider’s integrated proxy rotation and fingerprint management kept the failure rate under 3%. Crawl4AI, without built-in proxy infrastructure, dropped 28% of the anti-bot URLs. These failures cascade in production: a missing page means a missing chunk in your vector store, which means a wrong answer from your RAG pipeline.

Cost per 1,000 pages

Spider’s cloud API is 4.6x cheaper than Firecrawl’s cloud. For self-hosted deployments, Spider’s Rust binary consumes roughly one-third the CPU and memory of a Node.js or Python process doing the same work, which translates to smaller instances and lower bills.

Crawl4AI is free to use, and the self-hosted compute cost is reasonable. The hidden cost is engineering time: you build and maintain the proxy layer, the retry logic, the scaling infrastructure, and the monitoring. For teams that have that capacity, it is a legitimate option. For teams that don’t, the “free” label is misleading.

Markdown quality (RAG retrieval accuracy)

This metric matters more than most benchmarks acknowledge. If the markdown is noisy (nav bars, cookie banners, footer links, boilerplate), your embeddings carry that noise, and retrieval quality degrades.

All three produce usable markdown. The differences are at the margins, but margins compound. A 7-point gap in recall@5 means your users get wrong or incomplete answers roughly 1 in 14 queries more often with Crawl4AI output than with Spider output. Over thousands of daily queries, that adds up.

Firecrawl’s markdown is genuinely good. It handles article content well and strips most boilerplate. Spider edges it out on noisier pages (e-commerce, forums, documentation with heavy sidebars) where the Rust parser’s content extraction heuristics are more aggressive.

Crawl4AI provides knobs to tune extraction (CSS selectors for exclusion, custom chunking), but the defaults are more permissive. Teams willing to spend time configuring per-domain rules can close the gap.

Time to first result

Spider returns the first static page result in under 50 milliseconds. For interactive applications (chatbots that fetch context on demand, agents that browse in real time), this is the difference between a responsive experience and a loading spinner.

The gap on static pages is almost entirely architectural. Spider’s HTTP client is compiled Rust with zero-copy parsing. Firecrawl’s Node.js runtime and Crawl4AI’s Python asyncio loop both add overhead before the first byte is even processed.

Summary table

Code comparison

Here’s the same operation (scrape a URL, get markdown) in all three tools.

Spider (Python SDK)

import requests
import os

response = requests.post(
    "https://api.spider.cloud/crawl",
    headers={
        "Authorization": f"Bearer {os.getenv('SPIDER_API_KEY')}",
        "Content-Type": "application/json",
    },
    json={
        "url": "https://example.com",
        "limit": 10,
        "return_format": "markdown",
        "request": "smart",
    },
)

for page in response.json():
    print(f"{page['url']}: {len(page['content'])} chars")

Spider (Rust crate, self-hosted)

use spider::website::Website;
use spider::configuration::Configuration;

#[tokio::main]
async fn main() {
    let mut config = Configuration::new();
    config.with_limit(10);
    config.with_return_page_links(true);

    let mut website = Website::new("https://example.com")
        .with_configuration(config)
        .build()
        .unwrap();

    website.crawl().await;

    for page in website.get_pages().unwrap().iter() {
        let markdown = page.to_markdown();
        println!("{}: {} chars", page.get_url(), markdown.len());
    }
}

Firecrawl (Python SDK)

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key="fc-YOUR_API_KEY")

result = app.crawl_url(
    "https://example.com",
    params={
        "limit": 10,
        "scrapeOptions": {
            "formats": ["markdown"],
        },
    },
    poll_interval=2,
)

for page in result.get("data", []):
    print(f"{page['metadata']['url']}: {len(page.get('markdown', ''))} chars")

Crawl4AI (Python, self-hosted)

import asyncio
from crawl4ai import AsyncWebCrawler

async def main():
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(url="https://example.com")
        print(f"{result.url}: {len(result.markdown)} chars")

asyncio.run(main())

Note that Crawl4AI’s arun processes a single URL. Crawling multiple pages with link following requires additional code to manage the URL frontier, deduplication, and concurrency. Spider and Firecrawl handle this with a single limit parameter.

What the benchmarks don’t show

Numbers on a page only capture part of the story. Here are the dimensions that didn’t fit neatly into a table but matter when you’re choosing a tool for production.

Maintenance burden

Spider is a managed service with a Rust core. You send API requests and get results. Proxy management, browser pools, anti-bot bypass, retries: all handled on the platform side. The open source crate is a single binary with no runtime dependencies beyond libc.

Firecrawl offers a managed cloud, but the open source version requires you to run Redis, a Node.js server, Playwright browsers, and optionally a separate worker process for async jobs. That’s multiple moving parts to keep healthy.

Crawl4AI is self-hosted only. You’re responsible for everything: browser lifecycle management, proxy rotation, error handling, horizontal scaling, and monitoring. For a prototype or a research project, this is fine. For a production service processing millions of pages, it is a significant engineering commitment.

Scaling pain

Scaling a scraping workload from 100 pages to 100,000 pages is where architectural choices become obvious.

Spider’s Rust engine was designed for this from the start. The async runtime (tokio) handles tens of thousands of concurrent connections with predictable memory usage. The cloud API scales horizontally behind the scenes.

Firecrawl’s Node.js runtime handles concurrency well at moderate scale, but memory usage grows with each browser context. The cloud service manages this for you; self-hosted requires careful tuning.

Crawl4AI’s Python/asyncio model works for hundreds of concurrent requests. Beyond that, you hit Python’s GIL limitations and Playwright’s browser memory overhead. Scaling to tens of thousands of concurrent connections means running multiple processes behind a task queue, which you build and maintain yourself.

Community and ecosystem

Spider has a smaller community (2,200+ GitHub stars on the Rust crate) compared to Firecrawl and Crawl4AI. SDKs exist for Python, JavaScript, and Rust. The MIT license means no restrictions on commercial use or derivative works.

Firecrawl has 30,000+ GitHub stars and strong community momentum. The AGPL-3.0 license is an important consideration: if you modify the source and offer it as a service, you must release your changes. For many companies, this means the cloud API is the only practical option.

Crawl4AI has 30,000+ GitHub stars and an active community building extraction strategies and sharing configurations. The Apache 2.0 license is permissive for commercial use. The trade-off is that without a managed service, community support forums become your primary troubleshooting resource.

Licensing matters

This deserves its own callout. The license you choose for your scraping layer affects your entire stack.

If you’re building a commercial product that incorporates a scraping tool, the AGPL clause on Firecrawl is worth discussing with your legal team. Spider’s MIT and Crawl4AI’s Apache 2.0 carry no such obligation.

Where each tool shines

Being fair means acknowledging that no tool is the best choice for every scenario.

Firecrawl is a good choice when you want a polished cloud API with solid markdown quality and don’t need maximum throughput. The developer experience is smooth, the documentation is thorough, and the crawl/scrape/map API surface is well-designed. If your workload is under 50,000 pages/month and you value simplicity over speed, Firecrawl delivers.

Crawl4AI is a good choice when you’re prototyping an AI pipeline on a budget, need full control over the extraction logic, or are doing research where cost must be zero. The Python ecosystem means you can plug it directly into your ML workflow without crossing language boundaries. If you have the engineering bandwidth to run infrastructure, it is genuinely capable.

Spider is the right choice when throughput, cost, and reliability at scale are the deciding factors. If your workload is measured in hundreds of thousands or millions of pages, if latency matters for your user experience, if you want a single API call that handles anti-bot, rendering, and markdown conversion, or if you need the permissiveness of MIT licensing, Spider is built for that.

Spider’s extraction models

Spider runs its own models for structured text-to-JSON extraction, handling most extraction workloads without calling external LLM APIs. This cuts both latency and per-token costs from the critical path.

Conclusion

On our test corpus, Spider led on throughput, cost, and success rate. Firecrawl produced good markdown and offers a polished developer experience. Crawl4AI is free and flexible for teams with the engineering capacity to run it.

These numbers reflect a single benchmark run against a specific set of URLs. Your results will vary based on target sites, geographic proximity, proxy quality, and workload characteristics. We encourage you to run your own comparison with URLs that match your production traffic.

These numbers reflect a specific URL list run on a specific day. Anti-bot configurations change, cloud API performance fluctuates, and your target sites will behave differently from ours. Run the benchmark yourself with your actual production URLs before making a decision.