Web scraping companies have to keep data flowing while websites block requests, proxies fail, servers go down, and deployments break working scrapers. Downloading HTML and extracting data is usually the easy part. Reliable delivery is the difficult part.
What makes running a web scraping company difficult?
Running a web scraping company means dealing with two kinds of problems at once: collecting the data and keeping the service alive. Clients see an API response, a CSV file, or fresh records in their system. They don't see everything required to make that delivery happen every day.
In our work, the recurring problems are:
- CAPTCHAs, browser fingerprint checks, rate limits, and blocked requests.
- Servers and critical processes that fail.
- Proxy outages, poor proxy quality, and exhausted balances.
- Deployments that damage shared infrastructure.
- Failures that happen overnight or during a holiday.
- Prospects who take a custom sample and then disappear.
Getting the data once proves very little. Keeping it flowing is the job.
Why isn't generic web scraping advice enough?
Generic web scraping advice names useful tactics, but it does not tell you how to build a reliable service for a specific website. The missing details are the ones that take years of testing, mistakes, blocks, and rebuilds to learn.
You can find plenty of advice online:
- Use real browsers.
- Keep cookies when sending HTTP requests.
- Rotate proxy IPs.
- Use residential proxies.
- Make your traffic look more natural.
None of that is wrong. It is also nowhere near a complete solution.
Every scraping company has its own methods for dealing with blocks and detection systems. Companies that make money from those methods rarely publish the exact details. Those tools and operating methods are intellectual property.
Would you tell everyone where you dig for gold?
I doubt it.
Why is recurring data delivery harder than scraping once?
A successful scrape shows that the collector worked at one moment. Recurring delivery must also work tomorrow, next weekend, and on Christmas morning.
One or two servers do not give you continuity. Many things can interrupt a working service:
- A hosting provider shuts down a server after noticing the scraping activity. Yes, this happens.
- A scraper sends traffic without the intended proxy and causes problems for the provider.
- Someone forgets to pay for a server. This also happens. We are only human.
- A whole data center has an outage.
- A server stays online while one critical process dies.
Having a server is not infrastructure. It is only the beginning.
What happens when a proxy service fails?
When a proxy service fails, requests can stop even if the scraper and its servers are healthy. A proxy is another dependency, and every dependency can fail.
In practice, we have to plan for several problems:
- The proxy provider has a technical failure.
- A faulty scraper burns through the available balance.
- Someone forgets to pay the invoice.
- A low-balance or expiry alert never reaches us.
- Proxy quality changes overnight, so requests that worked yesterday are suddenly blocked.
The client bought the service from us. They should not have to care whether our server provider had an outage or our proxy network failed.
In their eyes, we are responsible.
That is fair...?
How can one bad deployment break other scraping jobs?
One bad deployment can consume shared resources and disrupt jobs that were working perfectly well. Even a small scraper change can damage the rest of the infrastructure.
A faulty scraper can:
- Drain the proxy balance in minutes.
- Take all available CPU and memory from a machine.
- Flood a database with writes.
- Fill a message queue or create millions of duplicate jobs.
- Break a downstream data aggregation process.
A production scraper is rarely one script running quietly in the background. It may depend on databases, message queues, schedulers, storage, APIs, aggregation systems, monitoring, and alerts.
That means a deployment needs a safe way to stop a dangerous job and restore the last working version. Otherwise, one mistake can spread far beyond one scraper.
What should web scraping monitoring detect?
Web scraping monitoring should detect whether the expected data was collected and delivered, not merely whether a server responds. A green server status tells you very little when a critical process is dead.
Useful monitoring needs to catch problems such as:
- A scheduled job that did not start or finish.
- A sudden drop in successful requests or collected records.
- More errors, retries, or blocked requests than usual.
- Unexpected proxy usage or server load.
- A growing queue or delayed delivery.
Depending on the failure, the system may restart a process, stop a dangerous job, switch to another server, disable a broken scraper, or alert the right person. Automated recovery also needs limits. Endless retries can turn one failure into a much more expensive failure.
The worst incidents happen while you sleep. Or during a holiday, exactly when you finally want to rest.
Scraping has a special sense of humor.
How does downtime affect client relationships?
Repeated downtime costs trust and can eventually cost the client. One short interruption is not the same as a pattern of unreliable delivery, but patience runs out when the same failures keep returning.
The client needs to know what happened, which data was affected, and whether missed data can be recovered. For recurring work, both sides should agree on the refresh schedule, what counts as a completed run, and how missed runs will be handled.
Clients rarely see this part of the job. They see the output. They do not see the servers, failed jobs, alerts, repairs, and backup plans behind it.
Why are data samples and payments a business risk?
Custom data samples cost time and infrastructure even when the prospect never becomes a client. We have prepared work for people who promised to pay for a sample and then did not pay.
Before receiving the sample, some prospects answer within minutes. After receiving it, every reply suddenly takes a week, if it comes at all.
Nothing physically stops a prospect from taking a sample and disappearing. That is why free evaluation data and paid custom work need a clear boundary. Before starting, agree on:
- The target website and requested fields.
- The sample size and delivery format.
- Whether the sample is free or paid.
- The price and payment timing for paid work.
- What happens after the prospect reviews the sample.
We spend time and money doing this work. The person on the other side may simply not care.
That is another part of the scraping business nobody likes to mention.
FAQ
What is the hardest part of running a web scraping company?
The hardest part is maintaining reliable delivery while websites and infrastructure change. Extracting fields from a page is only one step. Recurring work also needs monitoring, maintenance, and recovery when something fails.
Do proxies solve every web scraping problem?
No. Proxies can help with one part of the request path, but they do not fix scraper bugs, stopped jobs, database problems, or incorrect data. Proxy providers can also fail or change the quality of their service.
Why can a scraper work today and fail tomorrow?
A website can change its pages or blocking methods. A proxy service can degrade, a plan can expire, a process can stop, or a deployment can introduce a bug. A previously successful request does not guarantee the next one.
Is a successful data sample proof that recurring scraping will be reliable?
No. A sample shows which fields and format were available during one collection. Recurring delivery also depends on scheduling, monitoring, infrastructure, maintenance, and a recovery plan.
What should a client define before ordering recurring web scraping?
The client and provider should agree on the target websites, required fields, approximate volume, refresh frequency, delivery format, and how failed or missed runs will be handled. They should also define the scope and payment terms for custom sample work.
So, is web scraping easy?
Web scraping looks simple because the visible part can be reduced to two steps:
- Download the HTML.
- Extract the data.
Everything else determines whether the client receives useful data tomorrow.
Easy, right?
Don't make me laugh.
