ScraperAPI vs Firecrawl(2026)
ScraperAPI is better for teams that need simple to integrate. Firecrawl is the stronger choice if purpose-built for ai/llm workflows. ScraperAPI is freemium (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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ScraperAPI
ScraperAPI is a web scraping API that manages proxies, browsers, and CAPTCHAs so you can fetch any page with a single request. It rotates across millions of proxies, retries failed requests automatically, and offers structured data endpoints for search engines and e-commerce sites.
Starting at $49/month
Visit ScraperAPIFirecrawl
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 ScraperAPI and Firecrawl Compare on Features?
| Feature | ScraperAPI | Firecrawl |
|---|---|---|
| Pricing model | freemium | freemium |
| Starting price | $49/month | $16/month |
| Single-endpoint scraping API | ✓ | — |
| Automatic proxy rotation (datacenter + residential) | ✓ | — |
| JavaScript rendering | ✓ | ✓ |
| Automatic retries | ✓ | — |
| Geotargeting | ✓ | — |
| Structured data endpoints (Google, Amazon) | ✓ | — |
| Async scraping for large jobs | ✓ | — |
| Scrape, crawl, map, and extract endpoints | — | ✓ |
| Clean Markdown + structured JSON output | — | ✓ |
| LLM-ready output for RAG pipelines | — | ✓ |
| Automatic proxy + anti-bot handling | — | ✓ |
| SDKs for Python and Node | — | ✓ |
| LangChain / LlamaIndex integrations | — | ✓ |
ScraperAPI Pros and Cons vs Firecrawl
ScraperAPI
Firecrawl
Deep dive: ScraperAPI
When to choose ScraperAPI
ScraperAPI is the right choice when the team needs a simple, proxy-based scraping API with automatic retry logic and good success rates, and when the budget is moderate. It sits between ScrapingBee and Bright Data in both complexity and price: simpler than Bright Data's product suite, more capable than ScrapingBee's basic offering for structured data extraction. The auto-retry on failed requests is a genuine differentiator: ScraperAPI automatically retries requests with different proxies and configurations until it gets a successful response or exhausts the retry budget, which significantly improves success rates on moderately protected sites. The structured data endpoints for Google SERP, Amazon product pages, and e-commerce sites return JSON directly, eliminating the need to parse HTML. Choose ScraperAPI when the team wants a reliable scraping API with a free tier for prototyping and structured endpoints for common targets. Avoid it when the targets require premium residential proxies for every request or when the team needs a full scraping platform with scheduling, storage, and workflow orchestration.
Real-world use case
An SEO agency uses ScraperAPI to monitor search rankings for 200 client keywords daily. Each keyword triggers a request to the Google SERP endpoint, which returns structured JSON with the top 10 results including title, URL, and snippet. The agency's internal dashboard processes the JSON and tracks ranking changes over time. The daily workload is 200 requests, all using the structured SERP endpoint at 5 credits each, totaling 1,000 credits per day or 30,000 credits per month. On the Hobby plan at per month with 100,000 credits, the agency has room to scale to 600 keywords before needing to upgrade. The tradeoff: the SERP endpoint costs 5 credits per request compared to 1 credit for basic HTML scraping, so keyword monitoring is 5x more expensive per request than general page scraping.
Hidden gotchas
Credit costs vary by feature flag: a basic HTML request costs 1 credit, JavaScript rendering costs 5 credits, geotargeting costs 5 credits, and premium residential proxies cost 10 to 25 credits. Combining JavaScript rendering with premium proxies costs 25 credits per request, making high-volume scraping of JS-heavy sites unexpectedly expensive. The free tier includes 1,000 credits per month, which is enough for about 200 JS-rendered requests. This is generous for prototyping but misleading if the team assumes it scales linearly. The auto-retry feature is valuable but consumes credits for each retry attempt, and a request that eventually fails after 3 retries has consumed 3 credits, not zero. The async scraping mode for large batch jobs requires polling a status endpoint for completion, and the documentation does not clearly specify the retention period for completed results. Geotargeting is available for 15 countries on the basic plan and all countries on higher plans, and requests targeting unavailable countries silently fall back to a US-based proxy.
Pricing breakdown
The free plan includes 1,000 credits per month. The Hobby plan at per month includes 100,000 credits. The Startup plan at per month includes 250,000 credits. The Business plan at per month includes 1,000,000 credits. A team making 50,000 basic HTML requests and 10,000 JavaScript-rendered requests per month consumes 50,000 plus 50,000 equals 100,000 credits, fitting on the Hobby plan. Adding geotargeting to the JS requests increases consumption to 150,000 credits, requiring the Startup plan.
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 ScraperAPI or Firecrawl?
For most teams, ScraperAPI is the better default: it offers simple to integrate and is freemium (from $49/month). Choose Firecrawl instead if purpose-built for ai/llm workflows matters more than js rendering and geotargeting cost extra credits. There is no universal winner — the right pick depends on your budget, team size, and whether you value simple to integrate or purpose-built for ai/llm workflows more.
Choose ScraperAPI if…
- •Simple to integrate
- •Free tier with 1,000 credits/month
- •High success rate with auto-retries
Choose Firecrawl if…
- •Purpose-built for AI/LLM workflows
- •Clean Markdown output saves prep work
- •Free tier with 1,000 credits/month