ScrapingBee vs Firecrawl(2026)
ScrapingBee is better for teams that need very simple api — fast to integrate. Firecrawl is the stronger choice if purpose-built for ai/llm workflows. ScrapingBee is paid (from $49/month) and Firecrawl is freemium (from $16/month).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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ScrapingBee
ScrapingBee is a developer-focused web scraping API that handles headless browsers, proxy rotation, and CAPTCHA avoidance behind a single endpoint. You send a URL and get back rendered HTML or extracted data — no infrastructure to manage.
Starting at $49/month
Visit ScrapingBeeFirecrawl
Firecrawl is a scraping and crawling API built for AI workflows — it crawls a site and returns clean Markdown or structured JSON ready to feed into LLMs and RAG pipelines. It handles JavaScript, proxies, and rate limits, and exposes scrape, crawl, map, and extract endpoints.
Starting at $16/month
Visit FirecrawlHow Do ScrapingBee and Firecrawl Compare on Features?
| Feature | ScrapingBee | Firecrawl |
|---|---|---|
| Pricing model | paid | freemium |
| Starting price | $49/month | $16/month |
| Single API endpoint | ✓ | — |
| JavaScript rendering (headless Chrome) | ✓ | — |
| Automatic proxy rotation | ✓ | — |
| Premium + residential proxies | ✓ | — |
| Google search API | ✓ | — |
| AI-powered data extraction | ✓ | — |
| No-block guarantee on credits | ✓ | — |
| Scrape, crawl, map, and extract endpoints | — | ✓ |
| Clean Markdown + structured JSON output | — | ✓ |
| LLM-ready output for RAG pipelines | — | ✓ |
| JavaScript rendering | — | ✓ |
| Automatic proxy + anti-bot handling | — | ✓ |
| SDKs for Python and Node | — | ✓ |
| LangChain / LlamaIndex integrations | — | ✓ |
ScrapingBee Pros and Cons vs Firecrawl
ScrapingBee
Firecrawl
Deep dive: ScrapingBee
When to choose ScrapingBee
ScrapingBee is the right choice when the team wants the simplest possible scraping API: a single endpoint that accepts a URL and returns rendered HTML, with proxy rotation and JavaScript execution handled automatically. It fits teams that do not want to manage browser infrastructure, proxy pools, or anti-bot logic, and where the scraping volume is moderate, roughly 10,000 to 500,000 pages per month. The API is stateless and requires no SDK: a simple HTTP GET with the URL and API key returns the page content. This makes it the fastest to integrate into existing codebases. The credit-based pricing is predictable: each request costs 1 credit for basic HTML, 5 credits for JavaScript rendering, and 10 to 25 credits for premium proxies. Choose ScrapingBee when the team needs a drop-in scraping API that works immediately. Avoid it when the volume exceeds 500,000 pages per month where per-credit pricing becomes expensive, or when the team needs scheduling, data storage, or workflow orchestration built into the scraping platform.
Real-world use case
A marketing team at a SaaS startup uses ScrapingBee to monitor competitor pricing pages. A daily cron job in a Next.js API route sends 50 URLs to ScrapingBee's API with JavaScript rendering enabled, parses the returned HTML for pricing table data using Cheerio, and stores the results in Supabase. The entire setup is 40 lines of TypeScript. Each request consumes 5 credits for JS rendering, so the daily cost is 250 credits. On the Freelancer plan at per month with 150,000 credits, the team has ample headroom for growth. The tradeoff: ScrapingBee returns raw HTML, so the team must write and maintain their own parsing logic. Apify or Firecrawl would extract structured data automatically. Additionally, if a competitor site changes its HTML structure, the parsing breaks silently, and the team only discovers the issue when they notice stale data.
Hidden gotchas
Credit consumption varies dramatically based on options selected per request. A basic HTML request costs 1 credit, but enabling JavaScript rendering increases it to 5. Enabling premium proxies on top of JS rendering costs 10 to 25 credits per request. A team that enables both options on all requests will burn through their credit allocation 10 to 25 times faster than one using basic HTML only. The AI data extraction feature, which uses an LLM to extract structured data from HTML, costs additional credits per field extracted. There is no free tier: the trial includes a limited number of credits, and after they are exhausted, a paid plan is required. The Google Search API endpoint costs 5 credits per search and returns SERP results in JSON, but the results are not always identical to what a real browser session returns due to proxy geolocation differences. Rate limiting on the API is not clearly documented for lower-tier plans, and teams that send concurrent requests above the plan limit receive 429 errors without a retry-after header.
Pricing breakdown
The Freelancer plan at per month includes 150,000 credits. The Business plan at per month includes 1,000,000 credits. A team making 10,000 basic HTML requests and 5,000 JavaScript-rendered requests per month consumes 10,000 plus 25,000 equals 35,000 credits, fitting comfortably on the Freelancer plan. The same volume on Apify would cost roughly to in compute units for HTTP-based crawling, making Apify significantly cheaper for high-volume, simple scraping.
Deep dive: Firecrawl
When to choose Firecrawl
Firecrawl is the right choice when the primary use case is feeding web content into AI or LLM pipelines. Its core differentiator is outputting clean Markdown and structured JSON from any web page, eliminating the HTML-to-text preprocessing step that every RAG pipeline and fine-tuning dataset otherwise requires. The API supports four modes: scrape (single page), crawl (follow links from a starting URL), map (discover all URLs on a domain), and extract (structured data extraction with schema). The free tier includes 1,000 credits per month, making it accessible for prototyping. Choose Firecrawl when building RAG applications, training data pipelines, or any workflow that needs clean text from web pages. It is also strong for documentation crawling, where the output feeds directly into a vector database. Avoid it when the scraping target requires complex interaction like form filling, pagination with infinite scroll, or login-protected content, where a full browser automation tool like Playwright or Apify is more appropriate.
Real-world use case
A developer building a customer support chatbot uses Firecrawl to ingest an entire product documentation site into a Pinecone vector database. The crawl endpoint is pointed at the docs root URL with a maximum depth of 3, and Firecrawl returns each page as clean Markdown with metadata including the page title, description, and canonical URL. The developer chunks the Markdown output, generates embeddings via OpenAI, and upserts them into Pinecone. The entire ingestion pipeline is 60 lines of Python. The documentation site has 400 pages, consuming 400 credits on the free tier. The developer schedules a weekly re-crawl to catch documentation updates. The tradeoff: Firecrawl's Markdown output is clean but not always structurally perfect. Code blocks, nested lists, and complex tables can lose formatting nuance. The developer adds a post-processing step to fix code block language tags, adding about 10 lines of Python.
Hidden gotchas
Credits do not roll over between billing periods on monthly plans. If the team does not use all 1,000 free credits in a given month, they are lost. The crawl endpoint follows links by default, and without a URL filter or maximum page limit, a crawl of a large site can consume thousands of credits unexpectedly. The extract mode, which uses an LLM to extract structured data based on a schema, costs more credits per page than simple scraping and the LLM extraction quality varies based on page complexity. The Markdown output uses an internal HTML-to-Markdown converter that handles most common patterns well but can produce unexpected output for pages with heavy use of CSS-based content rendering, iframes, or shadow DOM components. The JavaScript rendering is enabled by default on all requests, which provides better content extraction but consumes more credits per page than a hypothetical no-JS mode. Rate limiting on the free tier caps concurrent requests, and the API does not return a retry-after header on 429 responses.
Pricing breakdown
The free plan includes 1,000 credits per month. The Hobby plan at per month includes 3,000 credits. The Standard plan at per month includes 100,000 credits. The Growth plan at per month includes 500,000 credits. A scrape request costs 1 credit. A crawl request costs 1 credit per page crawled. The extract endpoint costs 5 credits per page. A team crawling 5 documentation sites with an average of 200 pages each weekly would consume 4,000 credits per month, fitting on the Hobby plan. Adding extract mode to 500 pages per month increases consumption by 2,500 credits, pushing the total to 6,500 credits and requiring the Standard plan.
Should You Use ScrapingBee or Firecrawl?
For most teams, ScrapingBee is the better default: it offers very simple api — fast to integrate and is paid (from $49/month). Choose Firecrawl instead if purpose-built for ai/llm workflows matters more than no free tier (trial credits only). There is no universal winner — the right pick depends on your budget, team size, and whether you value very simple api — fast to integrate or purpose-built for ai/llm workflows more.
Choose ScrapingBee if…
- •Very simple API — fast to integrate
- •Handles JS rendering automatically
- •Good documentation
Choose Firecrawl if…
- •Purpose-built for AI/LLM workflows
- •Clean Markdown output saves prep work
- •Free tier with 1,000 credits/month