Scrape millions of pages daily without ever touching a server
When your data operation outgrows single‑threaded scripts and rate‑limited APIs, our distributed crawling infrastructure scales horizontally to match your throughput — automatically. From 10,000 to 50 million pages a day, you get the same schema‑validated data, delivered straight into your warehouse.
domains: 1500
concurrency: auto-scaled
proxy_pool: "residential + DC mix"
retry_logic: exponential-backoff
delivery:
format: "parquet"
destination: "s3://acme-raw/"
schedule: "hourly"
12,400 req/s
37s avg. latency
High‑throughput pipelines powering
What “large scale” looks like on our infrastructure
Large‑scale web scraping is a managed service built for teams that need to pull data from hundreds or thousands of domains, at a cadence that makes “hourly” feel slow. Instead of you stitching together crawlers, proxies, and retry logic, we provision a distributed fleet that auto‑scales to meet your throughput targets — and then we monitor it so you don't have to.
Whether you need real‑time pricing from 5,000 e‑commerce sites, or a daily snapshot of 10 million public records, the data lands in your existing storage, schema‑validated and ready for downstream processing.
- Handles 10,000 to 50M+ pages per day on demand
- Intelligent proxy rotation and CAPTCHA solving at scale
- Auto‑healing: failed jobs retried without manual intervention
- Direct delivery to S3, Snowflake, BigQuery, Kafka, or your API
- Backed by a throughput SLA and a dedicated scale‑ops team
Why “just run more servers” isn't a scale strategy
These are the problems that turn a successful proof‑of‑concept into an operational headache when volume triples overnight.
Concurrency doesn't scale linearly
Adding more threads creates IP contention, triggers rate limits, and exposes anti‑bot walls. Without adaptive routing, raw throughput plateaus fast.
Data quality decays under pressure
At 100,000 pages an hour, a 1% parse error means 1,000 malformed records. Without automated validation, your data team becomes a clean‑up squad.
Delivery becomes the bottleneck
Generating terabytes of clean data is only half the picture. If your pipeline can't land it in the warehouse at the same speed, you're paying for throughput you never use.
Infrastructure that scales with your ambition
Every large‑scale engagement is built on the same distributed engine that powers our API — but tuned to your exact volume, cadence, and delivery requirements.
Horizontally distributed crawling
A fleet of workers orchestrated across regions, with auto‑scaling rules tied to your queue depth — not to a fixed server count.
Adaptive anti‑bot intelligence
Proxy pools, browser fingerprints, and CAPTCHA solving are managed per domain — tactics that work on one site don't blindly spill onto another.
Throughput‑aware delivery
Data is streamed to your warehouse as it's scraped — not batched at the end of a run. That means zero lag between extraction and availability.
Schema‑validated at every row
Every record is checked against your schema before it leaves the pipeline — malformed rows are quarantined, not dumped into your data lake.
{
"metric": "queue_depth",
"scale_up_threshold": 500,
"scale_down_threshold": 50,
"max_workers": 800,
"cooldown_seconds": 180
}
From throughput target to live pipeline
Throughput scoping
We map your domains, page volumes, refresh cadence, and anti‑bot difficulty to provision the right infrastructure footprint.
Staging at 10% load
A scaled‑down version of the pipeline runs against a representative sample, validating schema, latency, and error rates before full ramp‑up.
Full ramp‑up
Workers are added incrementally while we monitor target‑site pressure. By the time you see the data, the pipeline is running at steady state.
Continuous scale‑ops
We adjust proxy mixes, retry strategies, and worker allocation as your target landscape evolves — without you filing a ticket.
Deliverables at each stage of scale
Infrastructure runbook
A document detailing the worker fleet, proxy configuration, and delivery topology — approved by your infrastructure team before a single line of crawl code runs.
10% load test results
Schema‑validated sample dataset, latency percentiles, and error logs from a controlled ramp‑up, shared in a joint review session.
Full‑scale production pipeline
Live pipeline running at your target throughput, with delivery streaming into your data warehouse and a monitoring dashboard you can access.
Weekly throughput reports + auto‑scale tuning
Weekly summaries of pages scraped, success rates, and any scaling adjustments made — keeping you informed, not involved.
Pay for the scale you need, not the servers you imagine
Scale Sprint
For teams that need a one‑time bulk extraction (backfill, migration, or a large one‑off research dataset).
- Fixed scope and total page volume
- Burst capacity provisioned on demand
- Single delivery, then pipeline winds down
Managed Throughput
For ongoing, high‑volume data needs where you want predictable throughput month over month.
- Guaranteed pages/day bandwidth
- Auto‑scaling within your tier
- 99.5% delivery SLA
Scale‑on‑Demand
For teams with unpredictable spikes — add burst capacity when you need it, only pay for what you use.
- Base capacity + burst credits
- Same‑day scale‑up requests
- No long‑term lock‑in
High‑volume data teams across industries
E‑commerce Price Intelligence
Hourly re‑scrapes of 10,000+ product pages across dozens of retailers, delivered to Snowflake for BI.
News & Media Monitoring
Real‑time article extraction from 50,000+ news domains, fed into NLP pipelines for sentiment analysis.
Travel & Hospitality
Fare and availability data refreshed every 15 minutes across global booking platforms, at millions of queries per day.
Financial Data Aggregation
Public filings, market data, and alternative datasets collected at scale with full audit trail and retention policies.
Large‑scale scraping vs. the typical alternatives
| Capability | Large Scale Web Scraping | Self‑serve API (capped) | In‑house cluster |
|---|---|---|---|
| Pages per day (sustained) | Up to 50M+ | 1–5M typical limit | Varies (ops burden) |
| Auto‑scaling based on queue depth | |||
| Managed proxy & CAPTCHA at scale | |||
| Schema validation on every row | |||
| Streaming delivery to warehouse | |||
| Engineering time from your team | Minimal | Some integration | Full‑time ops team |
“We stopped worrying about throughput after the first week”
"We went from struggling to scrape 50,000 pages a day in‑house to a managed pipeline that does 12 million without breaking a sweat. Our data engineers are finally doing data work again."
"The auto‑scaling is what sold us. We have a seasonal spike every November — the pipeline just absorbs it, and the bill only goes up for the days we actually burst."
"We were losing 12% of records to silent parse failures with our old script. Since switching, we get a daily validated dataset and a clean‑room quarantine log we can actually act on."
Streamed straight into your data estate
Data leaves our infrastructure and lands in yours — no intermediate tool, no new UI to learn.
Get a throughput estimate for your volume
Share your target domains and page counts — we'll model the cost and latency in under two business days.
Questions we hear from teams hitting scale limits
Tell us your throughput target — we'll tell you what's possible
Book a scoping call with a scale architect who has built pipelines that handle over a billion records a month. No slides, no pre‑packaged demos — just an honest conversation about your data volumes.
Most managed‑throughput pipelines are live within 3 weeks of scoping.
