companies of all sizes
See why teams choose
Firecrawl over ScrapeBadger.
When comparing Firecrawl vs ScrapeBadger, the difference comes down to clean data, one API for search and crawl, open source code, Firecrawl Alexandria data providers, and fewer tokens per page.
Clean data for AI agents
Firecrawl strips navigation, ads, and scripts and returns Markdown or schema-based JSON from any URL, so your AI agents read content instead of parsing HTML.
See use casesSearch, crawl, and interact in one API
One API key covers /search, /scrape, /crawl, /map, and /interact. ScrapeBadger scrapes one URL per request and spreads site data across separate scraper APIs.
View docsTurn entire websites into LLM-ready markdown or structured data.
Open source and self-hostable
Run Firecrawl on your own infrastructure with the full source code under the AGPL-3.0 license.
View on GitHubFirecrawl Alexandria and first-party indexes
With a Firecrawl API key on a team with Firecrawl Alexandria enabled, a search call that adds alexandria to its sources returns web results with Firecrawl Alexandria tools beside them: 400+ data providers such as SEC EDGAR, FRED, Fiscal.ai, and Benzinga, plus Firecrawl's Developer and Research indexes. ScrapeBadger's dedicated APIs scrape a fixed list of sites.
Explore Firecrawl AlexandriaFewer tokens on every page
Across 200 real URLs, Firecrawl's Markdown used over 90% fewer input tokens than raw HTML. The highlights and question formats go further, with up to 100x fewer tokens than a full scrape.
See token efficiencyFirecrawl leads on extraction quality.
And so much more.
Internally conducted benchmark, run Jan 13, 2026. Tested 1,000 URLs drawn from diverse public web domains (news, documentation, e-commerce, finance, and more) and measured whether each tool retrieved at least 10% of the expected content, defined as core page text, excluding navigation, ads, and footers. Dataset publicly available at the Firecrawl scrape-content-dataset-v1.
· Figures from the run of Jan 13, 2026
Measured performance
Published Firecrawl results, each with the dataset it was measured on and the date it was run. Follow a row to the run it comes from.
| Metric | Value | Dataset | Measured | Source |
|---|---|---|---|---|
| Coverage (success rate) | 96% | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Extraction accuracy (F1) | 0.638 | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Content recall | 0.639 | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
| Latency (P95) | 3,387 ms | Scrape coverage and quality, 1,000 URLs | Jan 13, 2026 | Methodology |
Scrape coverage and quality scored against the public dataset firecrawl/scrape-content-dataset-v1, so the inputs are checkable. The harness is not published yet, so the run cannot be reproduced end to end.
Every benchmark Firecrawl runs is listed on /benchmarks.
Firecrawl is purpose-built for
AI agents and developers.
The best ScrapeBadger alternative is one that returns Markdown by default, charges one rate per page, crawls whole sites, and searches the web with full page content.

“Firecrawl allows our customers to pull the web information they need directly in our product.”
“If your agent or LLM needs web content, Firecrawl delivers the best-formatted results.”
“What makes Firecrawl essential is how it turns messy web data into clean, AI-ready content.”
“It was just a matter of plugging in the Firecrawl API, feeding it a URL, and getting clean markdown in return.”
“Firecrawl is the easiest way to extract relevant content from a website.”
“We send over 15 million requests per month to Firecrawl, so every builder has the data they need to bring their app to life.”

















