Why I wrote this: While working with Korean public-data sources, I keep running into pages where the information is public but no convenient API exists. I have been using Firecrawl in those gaps. After seeing a similar policy-recommendation site, I also started thinking that these services probably need some kind of collection layer when the official API is missing.
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Summary
Since the title now compares Firecrawl, Crawl4AI, and WaterCrawl, I also want the body to compare all three. This is still not a synthetic benchmark run on the same URL set. It is a usage review based on my Firecrawl experience with public-data pages that have no API, strengthened with public metrics, self-hosting options, and the question of what happens when usage grows.
In this article
What this article covers
Why I started using Firecrawl
When dealing with public data, the most frustrating pattern is not that the data is hidden. It is often visible on a page, but not exposed through an API that is easy for developers to use. Some information is buried in search pages, some exists only on agency pages, and some useful fields are missing from the official API.
When a clean API exists, I still prefer it. But when there is no API for the data I need, I eventually need a way to read the page and convert it into text that an LLM or a small backend pipeline can use. That is where Firecrawl has been useful for me.
Firecrawl vs Crawl4AI vs WaterCrawl in practice
| Area | Firecrawl | Crawl4AI | WaterCrawl |
|---|---|---|---|
| Feel | More like calling a ready-made API. | More like running and tuning my own crawler. | More like growing a self-hosted crawling service or internal collector. |
| Strength | Hides much of the annoying work around JavaScript pages, Markdown conversion, and scraping infrastructure. | Gives more control over browser settings, sessions, cookies, extraction strategies, and deployment. | Looks attractive for recurring crawling, queues, storage, monitoring, and operational collection flows. |
| Performance in my use | Good for quickly checking one or a few pages and getting LLM-friendly text. | Looks better for repeated large-scale jobs where I own the pipeline. | Becomes interesting again when usage grows and self-hosted operation starts to matter. |
| Cost and operations | Easy to start with a free plan, but usage limits and credits matter as volume grows. | Open source and free as software, but server, browser, memory, proxy, and maintenance costs are mine. | Can start with free credits and has a self-hosted direction, but I still own the operations if I go that way. |
From other reviews and project discussions, Firecrawl is often praised because it makes the web feel like an API. Crawl4AI is closer to a controllable LLM-friendly crawler that developers can own. WaterCrawl sits in the conversation as a self-hosted, recurring-crawl oriented option. So the comparison is less about one being universally faster and more about managed convenience vs direct control vs recurring collection operations.
What the public numbers say
To compare real speed fairly, I would need to run the same URL set through all three tools under the same network and rendering conditions. I have not done that benchmark yet. So I separated the public, easy-to-check numbers from my own usage impression.
| Metric | Firecrawl | Crawl4AI | WaterCrawl |
|---|---|---|---|
| GitHub stars | About 140.9k | About 70.1k | About 1.9k |
| GitHub forks | About 8.1k | About 7.2k | About 235 |
| Open issues | About 367 | About 115 | About 6 |
| Self-hosting | Possible, but Firecrawl separates the hosted service from the open-source/self-hosted path. Operational effort and AGPL terms both matter. | Yes. Running it locally, on a server, or through its Docker/API server is one of its main strengths. | Yes. Its README and official site emphasize self-hosted and open-source operation. |
| License | AGPL-3.0 | Apache-2.0 | NOASSERTION/Other via GitHub API |
| Free starting point | Official pricing page: 1,000 credits / 1,000 pages per month, 2 concurrent requests | Open-source library; cost depends on my runtime and infrastructure | Official page: 1,000 page credits per month, 1 concurrent crawl |
| Performance claims | README claims 96% web coverage and 3.4s P95 latency | Emphasizes async browser pool, caching, and fewer hops, but performance depends on the environment | Emphasizes monitoring plus crawl speed/depth controls, but public benchmark numbers are limited |
Those numbers make Firecrawl look like the larger managed-service option, while Crawl4AI has a large open-source community behind it. WaterCrawl is much smaller by GitHub stars, but the free credits and self-hosted direction still make it a tool I would revisit if my crawling volume grows.
This row matters to me. If usage grows, whether I can self-host may matter more than the convenience of a paid API. That is why I can keep using Firecrawl for now while still watching Crawl4AI and WaterCrawl as longer-term self-hosted options.
When public data has no API
This is where Firecrawl currently fits my workflow. For public-data portals or agency pages where an API is not provided for the information I need, I can quickly turn the page into Markdown or structured text and inspect the result. That has been surprisingly convenient. The free plan also makes small experiments easy.
Of course, this does not replace official APIs. If I need scheduled large-scale collection, I still have to check terms, robots.txt, request frequency, and whether the data contains personal information. But for the first question — “Can this page be turned into usable LLM context?” — Firecrawl lowers the barrier a lot.
A similar policy-recommendation example
The LifeReference Korea policy recommender is a useful example. Users enter conditions and receive recommended government support programs or policy-fund information. The page has its own REST endpoint and appears to return recommendations from organized source data.
I cannot see the internal implementation of that site, so I should not claim that it definitely crawls data. But services like this often combine official APIs, their own database, manual curation, and crawling. When no official API exists for the target data, some collection pipeline is usually needed.
That is why it caught my eye. In reality, many useful public pages exist without a developer-friendly API. Firecrawl is helpful when I want to test that gap quickly.
Things to be careful about
Do not choose only by benchmark numbers
Firecrawl emphasizes fast responses and broad coverage, while Crawl4AI emphasizes async browser pools, caching, and detailed browser control. Actual performance still depends on the target site, JavaScript rendering, blocking rules, and request volume.
Crawling still has operational responsibility
Free and convenient does not mean I should crawl aggressively. Terms of service, robots.txt, rate limits, copyright, and privacy still matter. For public-policy data, I also want readers to check the original source before making real decisions.
Crawl4AI is free software, not free operations
Crawl4AI is attractive because it is open source under Apache 2.0. But I would still be responsible for browser execution, deployment, memory, sessions, and proxies. Firecrawl starts more easily, while Crawl4AI becomes attractive when I really need control.
Conclusion
For my current usage, Firecrawl still comes first. It is low-friction when I need to read public pages that do not expose the API I need and turn them into LLM-friendly text. If usage grows, I do find myself looking back at WaterCrawl because repeated crawling volume can change the cost and operations equation. Still, I have not had a serious problem with Firecrawl so far, and the free-start workflow is comfortable enough that I plan to keep using Firecrawl a bit longer.
So my practical conclusion is simple: Firecrawl is better for fast experiments and managed convenience; Crawl4AI is better for direct operation and fine control. For my current volume and purpose, continuing with Firecrawl feels natural.
References
- Firecrawl official site
- Firecrawl Scrape documentation
- Firecrawl GitHub repository
- Crawl4AI documentation
- Crawl4AI GitHub repository
- LifeReference Korea policy recommender