Scrape Large Websites Efficiently with Firecrawl in 2026
A practical guide to crawling large sites at scale with Firecrawl — map, async crawl jobs, batch scraping, deduplication, incremental updates, and proxy pairing.
Scraping a handful of pages is easy. Scraping hundreds of thousands of them — reliably, without timeouts, bans, or a runaway bill — is an entirely different engineering problem. As of 2026, the web has crossed 1.1 billion websites, and the data teams that win are the ones who can ingest large sites efficiently, not just eventually.
Traditional scrapers fall apart at scale. Single-threaded scripts crawl for days, JavaScript-heavy pages return empty HTML, one flaky network call kills the whole job, and a single IP gets blocked halfway through — leaving you with a half-finished dataset and no easy way to resume. Large-scale web scraping needs orchestration, not just a for-loop.
Firecrawl was built for exactly this. In this guide you will learn how to scrape large websites efficiently with Firecrawl: scoping a crawl with the map endpoint, running asynchronous crawl jobs, batch-scraping known URLs, deduplicating output, running incremental updates, and pairing proxies so massive crawls finish complete and clean.
What "Large-Scale" Scraping Actually Means
"Large" is not just a page count — it is the combination of volume, complexity, and recurrence. A 50,000-page documentation site, a marketplace with millions of dynamic listings, and a news archive you must re-crawl daily are three very different challenges, but they share the same failure modes: rate limits, JavaScript rendering, duplicate content, and partial failures.
Efficiency at scale comes down to four things: scoping (crawl only what you need), concurrency (parallelize without getting blocked), resilience (resume and retry instead of restarting), and cost control (do not pay to re-scrape unchanged pages). Firecrawl gives you primitives for all four, which is why it has become a default for scraping APIs built around modern AI and data pipelines.
Why Traditional Scrapers Break at Scale
Hand-rolled scrapers usually start as a synchronous loop over a list of URLs. That works for 100 pages and collapses at 100,000. Memory balloons as you hold everything in one process, a single unhandled exception aborts the run, and you have no way to resume from where it failed.
Then come the defenses. Modern sites render content with JavaScript, throttle by IP, and deploy anti-bot systems that serve CAPTCHAs to suspicious traffic. Without rendering, you get blank pages; without rotating IPs, you get blocked; without retries, you get gaps. Firecrawl absorbs rendering, retries, and link discovery, and pairs cleanly with a proxy layer for the IP rotation that large crawls demand.
Firecrawl Endpoints for Large Crawls
Choosing the right endpoint is the first efficiency decision. Each is optimized for a different stage of a large job.
Endpoint | Use at Scale | Returns |
|---|---|---|
/map | Discover and scope all URLs before crawling | URL list |
/crawl (async) | Crawl an entire site as a background job | Markdown per page |
/batch scrape | Scrape a known list of URLs in parallel | Markdown per URL |
/scrape | Refresh single changed pages | Markdown / JSON |
Step-by-Step: Crawl a Large Site Efficiently
Here is a resilient workflow for crawling a large site in Python. Each step maps to one of the four efficiency pillars above.
1Map the Site First to Scope It
Before spending a single crawl credit, use the map endpoint to discover every URL and estimate the job size. This lets you filter out sections you do not need.
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
site = app.map_url("https://big-site.com")
urls = site["links"]
print(f"Discovered {len(urls)} URLs")2Start an Asynchronous Crawl Job
For large sites, never block your script on a synchronous crawl. Kick off an async job and get back a job ID you can monitor — the crawl runs on Firecrawl infrastructure, not yours.
job = app.async_crawl_url(
"https://big-site.com",
params={
"limit": 5000,
"maxDepth": 4,
"scrapeOptions": {"formats": ["markdown"], "onlyMainContent": True},
},
)
job_id = job["id"]3Poll Status or Use Webhooks
Track progress with the status endpoint, or register a webhook so Firecrawl notifies you as batches complete instead of polling. Polling is simplest to start with.
import time
while True:
status = app.check_crawl_status(job_id)
if status["status"] == "completed":
break
print(f"Scraped {status['completed']} of {status['total']}")
time.sleep(10)
pages = status["data"]4Scope the Crawl with Path Filters
The single biggest efficiency win is crawling only what matters. Use include and exclude path patterns to skip logins, carts, and infinite filter pages that waste credits and time.
job = app.async_crawl_url(
"https://big-site.com",
params={
"limit": 10000,
"includePaths": ["/blog/.*", "/docs/.*"],
"excludePaths": ["/login.*", "/cart.*", "/search.*"],
"scrapeOptions": {"formats": ["markdown"]},
},
)5Batch-Scrape Known URLs in Parallel
When you already have a URL list (from the map step or a sitemap), batch scrape is faster and more predictable than a full crawl because it skips link discovery.
urls = ["https://big-site.com/a", "https://big-site.com/b"]
batch = app.batch_scrape_urls(
urls,
params={"formats": ["markdown"]},
)
results = batch["data"]6Deduplicate Before You Store
Large sites repeat headers, footers, and boilerplate across thousands of pages. Fingerprint each page and drop duplicates so you do not bloat storage or downstream processing.
seen, clean = set(), []
for page in pages:
text = page["markdown"]
fingerprint = hash(text)
if fingerprint in seen:
continue
seen.add(fingerprint)
clean.append({
"url": page["metadata"]["sourceURL"],
"text": text,
})7Run Incremental Re-Crawls
Never re-crawl an entire site to catch a few changes. Re-scrape only the URLs you know have updated, which keeps recurring jobs cheap and fast.
changed = ["https://big-site.com/updated-page"]
fresh = app.batch_scrape_urls(
changed,
params={"formats": ["markdown"]},
)If you are feeding this data into an AI app, the same output plugs straight into the pipeline in our guide on using Firecrawl for RAG applications.
Tuning Firecrawl for Speed and Cost
A few parameters control most of your crawl efficiency. Set them deliberately rather than accepting defaults on large jobs.
Parameter | Effect | When to Use |
|---|---|---|
limit | Caps total pages crawled | Always, to bound cost |
maxDepth | Limits how deep links are followed | Shallow content sites |
includePaths / excludePaths | Restricts crawl to relevant sections | Every large crawl |
onlyMainContent | Strips nav and boilerplate | Clean datasets |
Start conservative with a low limit on a test run, verify the output, then scale the limit up once your path filters are dialed in. This prevents burning credits on a misconfigured crawl.
Best Proxies to Pair with Firecrawl for Large Crawls
Firecrawl handles rendering and retries, but crawling millions of pages from protected or geo-restricted sources still needs IP rotation. A residential proxy layer keeps success rates high and prevents bans from punching holes in your dataset. These three pair well with high-volume crawls.
1Oxylabs
Oxylabs is the enterprise pick for massive crawls, with a 100M+ residential pool, 195+ countries, and a scraper API that complements Firecrawl on the most defended targets. Its anti-detection and SLA-backed uptime suit jobs that must run continuously at scale.
When a crawl spans heavily protected sites, routing through Oxylabs minimizes failed pages so your dataset stays complete.
2Decodo
Decodo (formerly Smartproxy) balances price and performance with a 97M+ residential pool and high success rates on tough targets. It is ideal for teams that need reliable, geo-targeted crawls without enterprise pricing.
Its clean dashboard and predictable pricing make it a strong default for recurring large crawls.
3IPRoyal
IPRoyal is the budget-friendly option, with pay-as-you-go traffic that never expires — perfect for irregular, bursty crawls where usage varies month to month. Pricing starts low while uptime stays solid.
Browse the full lineup in our proxy directory or compare the top residential proxies for web scraping.
Architecting a Resilient Large-Scale Crawl
Beyond a single crawl job, the biggest sites demand an architecture that parallelizes work and survives failure. These three patterns turn a fragile script into a system that finishes reliably, even across millions of pages.
1Split Big Sites into Parallel Scoped Jobs
Instead of one enormous crawl, partition the site by section and run several scoped jobs concurrently — for example, separate jobs for /blog, /products, and /docs, each with its own include path and limit. Parallel jobs finish faster, fail independently, and make cost attribution per section trivial. If one section errors out, the others still complete, so you never lose an entire run to a single bad branch of the site.
2Stream Results With Webhooks
Polling is fine to start, but at scale you want to process pages as they arrive rather than waiting for the whole job. Register a webhook so Firecrawl pushes each completed batch to your endpoint, where you can chunk, deduplicate, and store immediately. This keeps memory flat and turns a batch job into a streaming pipeline, which is essential when a crawl produces gigabytes of markdown.
3Checkpoint and Resume Instead of Restarting
Persist the job ID and the set of URLs you have already stored. If a downstream step fails, you can resume from your checkpoint and batch-scrape only the missing URLs rather than re-running the full crawl. For custom rotation logic between retries, our walkthrough on building a rotating proxy script in Python pairs well with this pattern.
Together, these patterns make large crawls predictable: parallelism for speed, webhooks for steady throughput, and checkpoints for recovery. Add a residential proxy layer on top and you can crawl even the most defended sites end to end without losing pages.
Common Mistakes to Avoid When Scraping Large Sites
These five mistakes turn an efficient crawl into a slow, expensive, incomplete one. Avoid them and large jobs become routine.
1Crawling Without Scoping
Pointing a crawler at a domain with no include or exclude paths is the fastest way to waste credits on logins, carts, and infinite filter URLs. Always scope with path patterns and a sensible limit before a full run.
2Using Synchronous Crawls for Huge Sites
A blocking crawl call on a 100,000-page site will time out or tie up your process for hours. Use async crawl jobs with status polling or webhooks so the work runs on Firecrawl infrastructure and your script stays responsive.
3Ignoring Credit and Cost Budgeting
Without a limit and path filters, costs scale with every link discovered. Preview size with the map endpoint, set a hard limit, and re-crawl incrementally instead of re-scraping unchanged pages.
4Skipping Deduplication
Boilerplate and near-duplicate pages inflate storage and corrupt downstream analytics. Fingerprint content and drop duplicates as part of ingestion, not as an afterthought.
5Crawling Protected Sites Without Proxies
Sending millions of requests from one IP triggers blocks and CAPTCHAs that silently drop pages. Route large crawls through residential proxies and follow the patterns in our bypass Cloudflare guide.
Best Practices for Large-Scale Firecrawl Crawls
Once your crawl runs, these habits keep it fast, complete, and cheap:
Map before you crawl — Always scope the URL set first so you know the job size and can filter aggressively.
Go async for anything big — Use background crawl jobs with webhooks instead of blocking calls.
Set a hard limit — Bound every crawl so a misconfiguration cannot run away with your budget.
Deduplicate and store metadata — Keep source URL and crawl date on every record for incremental updates and auditing.
Pair with proxies at scale — Route large or protected crawls through residential IPs; see web scraping at large scale for patterns.
Frequently Asked Questions
Conclusion: Crawling Large Sites the Efficient Way
Scraping large websites efficiently is less about raw speed and more about discipline: scope before you crawl, run jobs asynchronously, filter aggressively, deduplicate, and update incrementally. Firecrawl gives you every primitive needed to do this — map, async crawl, batch scrape, and single-page refresh — so you spend your time on the data, not the plumbing.
Pair it with a residential proxy layer for protected sources, set hard limits to control cost, and your crawls will finish complete, clean, and on budget — whether you are building a search index, a dataset, or an AI knowledge base.
The teams that scrape large sites successfully treat it as a data-engineering problem, not a scripting task. They measure crawl completeness, track cost per thousand pages, and monitor block rates the way an SRE watches uptime. With Firecrawl handling rendering and orchestration and a proxy layer handling IP rotation, that level of rigor becomes achievable for a small team — not just companies with a dedicated crawl-infrastructure group.
Ready to scale up? Start with Firecrawl, explore our proxy directory, and read our companion guide on using Firecrawl for RAG applications to turn your crawl into a production AI pipeline.