companies of all sizes
See why teams choose
Firecrawl over TinyFish.
When comparing Firecrawl vs TinyFish, the difference is reach across the pipeline (web pages and files alike), an open codebase, and purpose-built research and developer indexes on top.
Whole-site /crawl, batch scrape, /monitor, and /parse
Firecrawl walks entire sites with /crawl, discovers URLs with /map, processes many URLs concurrently with /batch/scrape, watches pages for changes with /monitor (only pings your agent when something meaningful moves), and turns uploaded PDFs, DOCX, XLSX, and HTML files into clean markdown with /parse. TinyFish Fetch caps at 10 URLs per request; there is no crawl, no map, no first-party /monitor endpoint, and no file parser.
See /crawl docsTurn entire websites into LLM-ready markdown or structured data.
Open source under AGPL-3.0 with 180K+ GitHub stars
The whole Firecrawl platform is open source with 180K+ stars on GitHub. Fork it, audit it, or run it in your own VPC for compliance and data residency. TinyFish publishes an open source cookbook of examples, but the core platform is a proprietary managed service.
See on GitHubPurpose-built Research and Developer indexes
On top of the live web API, Firecrawl maintains two curated indexes: the Research Index (43M+ paper abstracts, free to use) and the Developer Search Index (70M+ docs, READMEs, issues, and merged pull requests). TinyFish returns fresh live web results only; there are no pre-built specialty indexes for research or code.
See the Developer IndexFirecrawl leads on quality + token efficiency.
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
Token efficiency for coding agents.
Median LLM tokens a coding agent spends per task with each search API, on 100 hard retrieval tasks, search-only. Lower is better. Firecrawl ranks first of 14 configurations, essentially tied with TinyFish's second-place row.
Independent benchmark by OpenBenchmarks, published as most token-efficient web search API for coding agents. 100 hard retrieval tasks on documentation questions, run through each web search API with the same coding agent, model, and prompts. The metric is the median number of LLM tokens the agent spent per task, reported beside task completion. A pass requires a grounded source URL from that run. The study also publishes a separate search-and-fetch board, where the ranking differs. Runner and scoring are public at openbenchmarks-labs/web-search-for-coding-agents.
· Figures from the run of Sep 15, 2026
Firecrawl is purpose-built for
AI agents and developers.
In any Firecrawl vs TinyFish comparison, the difference is crawl and batch scale, an open source platform, broader SDK coverage, and retries that keep blocked pages in the corpus.

“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.”

















