Firecrawl Alternatives for LLM-Ready Web Data

The last row matters more than it looks. Firecrawl's JSON format adds credits on every page. If you already pay for a model API, running extraction yourself on clean Markdown moves that cost to a meter you already control and lets you use a smaller model for easy pages.

Firecrawl packages four stages that most data pipelines treat separately. Naming them is the fastest way to find the right replacement, because no single alternative matches Firecrawl stage for stage.

1. Discover. Map and crawl endpoints find URLs on a site. If you already have a sitemap, a product feed or a list from your own database, you are paying for discovery you do not use.
2. Fetch. Retrieve the page, render JavaScript when needed, and get past rate limits and bot checks. This is where proxies and browsers live, and where hard targets eat budget.
3. Convert. Turn HTML into Markdown or clean text with navigation, cookie banners and footers removed. This is the step people mean by "LLM-ready".
4. Extract. Ask a model to return a JSON object matching a schema. Useful, and the most expensive stage per page.

A useful exercise before evaluating anything: pull a week of your Firecrawl usage and count how many calls were map or crawl, how many were plain scrapes, and how many used JSON formats. If most of your spend turns out to be stages 2 and 3 on URLs you already knew, discovery is not what you are paying for. Our explainer on web scraping vs web crawling covers why those are different workloads with different cost shapes.

From firecrawl.dev/pricing, September 2026, with prices shown billed yearly:

• Free: 1,000 credits a month, 2 concurrent browsers, no card.
• Hobby: $16/month billed yearly, 5,000 credits, 5 concurrent.
• Standard: $83/month billed yearly, 100,000 credits, 25 concurrent.
• Growth: $333/month billed yearly, 500,000 credits, 50 concurrent.
• Scale: $599/month billed yearly, 1,000,000 credits, 100 concurrent.

Credit costs on the same page: scrape, crawl and map at 1 credit per page, search at 2 credits per 10 results, and JSON, question or highlight formats at +4 credits per page. Error pages such as 403 and 404 cost 1 credit, while failed scrapes with no result are not charged.

Two consequences follow. A crawl that returns 5,000 pages you did not need still bills 5,000 credits, so tight include and exclude paths matter. And JSON extraction turns a 1-credit page into a 5-credit page, which means the Standard plan's 100,000 credits cover 20,000 extracted pages.

The repository is licensed AGPL-3.0 for the main codebase, with SDKs under MIT, and its README states that the self-hosted version lacks some capabilities of the cloud service. If you planned to self-host to escape credits, read that comparison table first.

Which stages each option covers out of the box, based on each vendor's own documentation:

Read the "Discover" column first. If you need it and a tool does not have it, you are building a crawler, which is a real engineering project rather than a config change.

Crawl4AI

The open-source answer, and the most complete functional match. The repository lists Apache 2.0 licensing, Markdown generation with citation references, deep crawling with breadth-first, depth-first and best-first strategies, crash recovery, Playwright browser automation, CSS and XPath extraction, and LLM extraction against the provider of your choice. A Docker deployment with a FastAPI server exists.

Choose it when you have the engineering time to run browsers at scale and want a license you can ship inside a commercial product. The catch is that you now own fetch reliability. Crawl4AI supports proxy configuration, but it does not include proxies, so every blocked page is your problem. Budget for a proxy source and for browser fleet operations.

Jina Reader

The smallest possible integration: request r.jina.ai/ followed by the target URL and get LLM-friendly text back. Jina's page lists token-based pricing counted on output tokens, 20 requests per minute without a key, 500 RPM with a free key, 5,000 RPM on premium keys, and 10M free tokens on new keys.

Choose it when an agent reads individual pages on demand and you want no infrastructure at all. The catch is that token billing scales with page length, so long documentation pages cost far more than short product pages, and there is no crawler. Budget by measuring the token count of your real pages, not an average from someone else's corpus.

Apify Website Content Crawler

A crawler actor built for feeding models. Apify's page lists Markdown, text and HTML output, native LangChain and LlamaIndex integrations, and usage-based billing in compute units, with published approximations of about $0.20 per 1,000 pages on the raw HTTP crawler and $0.50 to $5 per 1,000 pages with a headless browser. The free Apify plan includes $5 of monthly credit.

Choose it when the job is "keep this documentation site in my vector store" and you want scheduling, storage and retries without writing them. The catch is variance: a browser crawl's cost depends on how heavy the site is, so the range is wide until you test your own targets.

Spider

A different meter again. Spider's pricing page lists pay-as-you-go at $1 per GB of pre-transformation content plus $0.0001 per CPU minute, no subscription requirement, unexpiring balances, and no charge for failed requests. A flat "Unlimited" option sells concurrent request slots, starting at $350 a month for 25.

Choose it when your volume swings month to month and your pages are light. The catch is that per-GB billing punishes heavy pages, the exact pages that are often worth crawling for RAG, such as long docs with embedded assets.

A general scraping API with Markdown output

ScrapingBee, ZenRows and SparkProxy all sell a fetch-and-render API, and some, including ZenRows and SparkProxy, return Markdown directly. None of them discovers URLs for you. If your URL list comes from sitemaps or your own systems, this class is usually the cheapest per page, because a plain fetch costs a single credit and there is no crawler margin. Our ZenRows alternatives comparison covers the credit ladders in this class in depth.

This is an illustrative model using published list prices and stated assumptions, not a benchmark we ran. Swap in your own numbers.

Assumed job: refresh a knowledge base of 50,000 known URLs once a month. 80% of pages fetch cleanly without JavaScript, 20% need rendering. Pages average roughly 1,500 output tokens of Markdown and about 300 KB before transformation. You want Markdown only, with extraction handled by your own model.

What the table actually shows is less about the winner and more about sensitivity:

• Firecrawl is insensitive to rendering and very sensitive to JSON extraction.
• Credit APIs with a render multiplier are cheap when most pages are static and get expensive quickly if your render share climbs from 20% to 80%.
• Token and byte meters track page size, so a corpus of long documentation behaves very differently from a corpus of short product pages.

So the question to answer with your own data is: what fraction of your pages need a browser, and how big are they? Measure that on 500 real URLs before choosing.

We sell one of the options above, so here is the plain version.

Fits: you already have your URLs, you want Markdown or HTML back, and most of your targets do not need a browser. The Scraping API at https://scrape.sparkproxy.io/api/v1 takes an X-API-Key header and returns html, md, mdx, json, screenshot or pdf through the format parameter. A plain fetch costs 1 credit and a JavaScript render costs 5. Plans run Starter $49 for 250,000 credits and 50 concurrent requests, Growth $99 for 1,000,000 and 100 concurrent, Pro $249 for 3,000,000 and 200 concurrent, Scale $599 for 8,000,000 and 400 concurrent. There are 1,000 free credits with no card.

Does not fit: you need a crawler. SparkProxy's API has no map or crawl endpoint. You also do not get LLM extraction: extract_rules takes CSS selectors, not a natural-language schema. If either of those is why you chose Firecrawl, keep it or pick Crawl4AI or Apify.

One detail to set explicitly: render_js defaults to true. For a Markdown pipeline on mostly static pages, pass render_js=false on every call, or every page bills at the rendered rate.

import requests

API = "https://scrape.sparkproxy.io/api/v1"

def page_markdown(url, render=False):
    r = requests.get(
        API,
        headers={"X-API-Key": "YOUR_API_KEY"},
        params={
            "url": url,
            "render_js": "true" if render else "false",  # 5 credits vs 1
            "format": "md",
        },
        timeout=120,
    )
    r.raise_for_status()
    return r.text

md = page_markdown("https://www.sparkproxy.io/docs/scraping-api/")
print(md[:500])

The comparison of paying per request against running your own fetchers is covered in web scraping API vs self-managed proxies.

If most of Firecrawl's value to you was crawl, but the sites you crawl publish sitemaps, you can replace discovery with a few lines of code. This keeps the crawler margin out of your bill and makes the URL list auditable.

import requests
import xml.etree.ElementTree as ET

NS = {"sm": "http://www.sitemaps.org/schemas/sitemap/0.9"}

def sitemap_urls(sitemap_url, include="/docs/"):
    xml = requests.get(sitemap_url, timeout=30).text
    root = ET.fromstring(xml)
    # Sitemap index: recurse into child sitemaps
    children = [n.text for n in root.findall("sm:sitemap/sm:loc", NS)]
    if children:
        out = []
        for child in children:
            out += sitemap_urls(child, include)
        return out
    return [n.text for n in root.findall("sm:url/sm:loc", NS) if include in n.text]

urls = sitemap_urls("https://www.sparkproxy.io/sitemap.xml")
print(len(urls), "URLs to fetch")

Pair that with lastmod from the same sitemap and you only refetch pages that changed, which does more for a monthly bill than any vendor discount. The approach is written up in incremental web scraping and change detection.

Sites without sitemaps are where a crawler earns its fee. Be honest about how many of your sources fall in that group before deciding you can drop one.

Markdown converters disagree more than their marketing suggests. Run the same 50 URLs through Firecrawl and your candidate, then check:

• Tables survive. Pricing and specification tables are the content models quote most. Broken tables are the most common failure.
• Code blocks keep their fences and language. A converter that flattens code into paragraphs damages documentation corpora badly.
• Boilerplate is actually removed. Cookie banners, "related articles" and footers inflate token counts and pollute retrieval.
• Links stay absolute. Relative links break citations in RAG answers.
• Soft blocks are caught. A 200 response that contains a challenge page is still a failure. Count words or check for a known selector, and never trust the status code alone. Our guide to detecting when your scraper is blocked lists the patterns.

Diff the Markdown outputs rather than eyeballing them. A 10% drop in average word count on the same URLs usually means content is being cut, not boilerplate.