Best Web Scraping APIs for LLMs and RAG Pipelines

Feeding live web data into LLMs and RAG systems changes the game — your AI has current context, not stale training data. But the scraping layer is where most RAG pipelines break. JavaScript-rendered pages, anti-bot protections, infinite scroll, and inconsistent HTML structures turn "just scrape it" into a multi-week engineering project.

This guide covers the best web scraping APIs purpose-built for LLM and RAG workflows — with pricing, code examples, and honest tradeoffs.

Key Takeaways

• LLM-optimized scraping means clean markdown or structured free JSON formatter, not raw HTML — token efficiency depends on it
• SearchHive ScrapeForge returns markdown with structured extraction — one API call produces LLM-ready content
• Firecrawl converts pages to LLM-ready markdown — solid product, expensive at scale ($83/month for 100K)
• Jina AI Reader is free for Markdown conversion — no API key needed for basic use, limited at volume
• Apify handles complex scraping (login, pagination, JS rendering) but pricing is compute-unit based and unpredictable
• Exa's Contents endpoint provides full page text — $1/1K pages, optimized for AI context windows
• The key metric is tokens per dollar, not pages per dollar — cleaner output means cheaper LLM inference

1. SearchHive ScrapeForge — Best for RAG Pipelines

ScrapeForge is built with LLM workflows in mind. It returns clean markdown by default, supports structured field extraction, and runs on the same credits as SearchHive's search and research APIs.

Pricing: Free (500 credits) → Starter $9/month (5K) → Builder $49/month (100K) → Unicorn $199/month (500K)

import requests

API_KEY = "your_searchhive_key"
BASE = "https://api.searchhive.dev/v1"

# Scrape a documentation page for RAG ingestion
resp = requests.get(f"{BASE}/scrape", params={
    "api_key": API_KEY,
    "url": "https://docs.python.org/3/library/asyncio.html",
    "format": "markdown",
    "extract": "title,section_headers,code_examples",
    "remove_links": True,
    "remove_images": True
})

content = resp.json()["content"]
# Clean markdown — ready for chunking and embedding
chunks = content.split("

")
print(f"Got {len(chunks)} chunks from documentation page")

For RAG workflows, the pattern is search then scrape:

import requests

API_KEY = "your_searchhive_key"
BASE = "https://api.searchhive.dev/v1"

# Step 1: Find relevant pages
search = requests.get(f"{BASE}/search", params={
    "api_key": API_KEY,
    "query": "FastAPI middleware authentication tutorial",
    "num_results": 5
})

# Step 2: Scrape top results for RAG context
context_parts = []
for result in search.json()["results"][:3]:
    scrape = requests.get(f"{BASE}/scrape", params={
        "api_key": API_KEY,
        "url": result["url"],
        "format": "markdown",
        "extract": "title,body",
        "max_chars": 5000
    })
    context_parts.append(scrape.json()["content"])

# Build RAG context — clean markdown, no HTML noise
rag_context = "

---

".join(context_parts)
print(f"RAG context: {len(rag_context)} characters from 3 sources")

The unified credit system means your search + scrape costs are predictable: the Builder plan gives you 100K total credits for $49/month regardless of how you split them between SwiftSearch and ScrapeForge.

2. Firecrawl — Solid Markdown Conversion

Firecrawl's core strength is converting any web page into clean, LLM-friendly markdown. It handles JavaScript rendering, removes navigation elements, and structures output for AI consumption.

Pricing: Free (500 credits) → Hobby $16/month (3K) → Standard $83/month (100K) → Growth $333/month (500K)

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key="your_firecrawl_key")

# Single page scrape — markdown output
result = app.scrape_url("https://docs.fastapi.dev/tutorial/first-steps/",
    params={"formats": ["markdown"], "excludeTags": ["nav", "footer"]}
)

markdown_content = result["markdown"]
print(markdown_content[:500])

Firecrawl also offers a crawl endpoint for site-wide scraping and a map endpoint for URL discovery. The product is mature and well-documented. The main drawback: at $83/month for 100K credits, you'll hit budget limits faster than with SearchHive ($49/100K).

3. Jina AI Reader — Free Markdown Extraction

Jina AI Reader converts URLs to Markdown via a simple GET request. No signup, no API key, no rate limits for light use.

Pricing: Free (basic) → $3/1K pages (higher limits) → Enterprise (custom)

import requests

# No API key needed — just prefix any URL
resp = requests.get(
    "https://r.jina.ai/https://example.com/documentation-page",
    headers={"Accept": "text/markdown"}
)

markdown = resp.text
print(markdown[:500])

Extremely simple to integrate. The limitations: no structured extraction, no batch processing, no JavaScript rendering for complex SPAs, and the free tier has undocumented soft rate limits. Good for prototyping or low-volume use; not suitable for production RAG pipelines.

4. Apify — Best for Complex Scraping

When your RAG pipeline needs to scrape sites that require login, handle infinite scroll, or navigate multi-step forms, Apify's Actor-based architecture handles the complexity.

Pricing: Free ($5 usage) → Starter $29/month → Scale $199/month → Business $999/month + $0.20-$0.30/CU

from apify_client import ApifyClient

client = ApifyClient("your_apify_token")

# Crawl a documentation site — handles sitemap, pagination, JS rendering
run = client.actor("apify/website-content-crawler").call(run_input={
    "startUrls": [{"url": "https://docs.example.com"}],
    "maxCrawlPages": 100,
    "maxCrawlDepth": 3,
    "crawlerType": "cheerio",
})

# Collect all scraped content for RAG
documents = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    documents.append({
        "url": item.get("url"),
        "text": item.get("text", "")[:2000],
        "metadata": {"title": item.get("title", "")}
    })

print(f"Crawled {len(documents)} pages for RAG ingestion")

The compute-unit pricing model makes costs unpredictable. A 100-page documentation crawl might use 5-50 CUs depending on page complexity and JavaScript rendering needs. Budget $50-200 for a single substantial crawl job.

5. Exa Contents — Full Page Text for AI Context

Exa's Contents endpoint fetches full page text optimized for LLM context windows. It's paired with their Search API for a find-then-fetch workflow.

Pricing: Contents $1/1K pages → Page summaries $1/1K pages

import requests

resp = requests.post("https://api.exa.ai/contents", headers={
    "x-api-key": "your_exa_key"
}, json={
    "ids": ["https://docs.python.org/3/library/asyncio-task.html",
            "https://fastapi.tiangolo.com/tutorial/dependencies/"],
    "text": {"maxCharacters": 5000}
})

for page in resp.json()["results"]:
    print(page["url"])
    print(page.get("text", "")[:200])
    print()

Cheap at $1/1K pages, but you need Exa's Search API ($7/1K) to discover the pages first. Combined cost: $8/1K for search + content, versus SearchHive's all-in credits.

6. ScrapingBee — Reliable Headless Scraping

ScrapingBee handles headless browser rendering, proxy rotation, and CAPTCHA solving. It returns raw HTML or extracted text.

Pricing: Freelancer $49/month (150K credits) → Startup $99/month (500K) → Business $249/month (2M)

import requests
import json

resp = requests.get("https://app.scrapingbee.com/api/v1/", params={
    "api_key": "your_scrapingbee_key",
    "url": "https://example.com/docs",
    "render_js": True,
    "extract_rules": json.dumps({
        "title": "h1",
        "content": {"selector": "main", "type": "text"}
    })
})

data = resp.json()
print(data.get("title"))
print(data.get("content", "")[:300])

ScrapingBee handles the infrastructure well but returns raw text, not structured markdown. You'd need post-processing to make it LLM-ready — adding development time and token overhead.

What Makes a Scraping API Good for RAG?

Not all scraping is equal when the consumer is an LLM. The factors that matter:

Token efficiency. Raw HTML wastes tokens on scripts, styles, and navigation. Markdown output trims 60-80% of noise. Every token saved is money saved on LLM inference.

Structured extraction. Pulling specific fields (title, date, author, body) directly in the API response eliminates post-processing code.

Reliability. A failed scrape means a gap in your RAG context. Built-in retries, proxy rotation, and JavaScript rendering matter.

Cost at volume. RAG pipelines need thousands of pages. Per-page cost determines whether the system is economically viable.

Comparison Table

The Verdict

For RAG pipelines, the scraping API's output format matters as much as its reliability. Raw HTML from ScrapingBee or generic text from Exa Contents requires post-processing before it's useful. Firecrawl and SearchHive ScrapeForge both output clean markdown — the difference is price.

SearchHive gives you search + scraping + research in one API for $49/month at 100K credits. Firecrawl's equivalent tier costs $83/month and has no search capability. For teams building RAG systems that need both content discovery and content extraction, the math is straightforward.

Start free — 500 credits, no card required. Scrape your first 100 documentation pages and see how clean markdown reduces your chunking headaches.