Why your scrapers keep breaking — and what to use instead

Last reviewed Aug 2026.

The short answer: scrapers break because they encode assumptions about a page’s structure, and pages change. Nothing fixes that permanently — the real choice is who does the repair. You can maintain them yourself, use someone else’s maintained scraper, pay a managed service, or use extractions that regenerate themselves when a source changes.

The rest of this page is about picking correctly, including when maintaining your own is the right answer.

Why they break

Six causes, roughly in order of how often they bite:

1. The DOM changes. A selector points at .product-price; a redesign renames it. This is the classic failure and the easiest to fix — and the most frequent, because front-end teams rename things constantly without any idea someone is depending on the old names.

2. The page moves to client-side rendering. Content that used to be in the HTML now loads via JavaScript after paint. Your scraper still gets a 200 and still parses successfully — it just parses an empty shell.

3. Pagination or navigation changes. Infinite scroll replaces numbered pages, a “load more” button appears, a filter moves into a query parameter. The scraper collects page one and stops, and nothing about that looks like an error.

4. Anti-bot escalation. Rate limits tighten, a CAPTCHA appears, fingerprinting gets stricter. Usually loud, at least.

5. The site restructures entirely. A new information architecture — the data still exists but on different pages, with different URLs.

6. Silent schema drift. The worst one. A field changes meaning rather than location: “price” starts including tax, a date format flips, a units column switches. Data keeps flowing, nothing errors, and the numbers are quietly wrong for weeks.

Notice how many of these fail silently. A scraper that crashes is a good scraper — you find out immediately. Most breakage produces plausible-looking output, which is why teams discover it downstream, after decisions were made on it.

What it actually costs

Teams underestimate this because they price the fix, which is usually an hour.

Detection lag. The expensive part isn’t repair, it’s the days or weeks between breaking and noticing. Whatever consumed that data — a dashboard, a pricing model, a lead score, a customer-facing feature — ran on bad input the whole time.

Trust decay. Once a pipeline has silently failed twice, people stop believing the outputs, which is far more expensive than the outage. A dataset nobody trusts has no value regardless of accuracy.

It scales with sources, not volume. Scraping ten million pages from three stable sites is easy. Scraping ten thousand pages across four hundred sites is a maintenance job for a person, because breakage frequency scales with the number of distinct sites, not with request count.

It lands on your most expensive people. Nobody staffs a dedicated scraper-maintenance role. The work goes to whichever engineer touched it last, as an interrupt, at the worst possible time.

The four options

1. Keep maintaining your own

Right when: you have a handful of stable, high-value sources; you already have the code; and someone owns it as a real responsibility rather than an interrupt.

Genuinely the correct answer for a lot of teams. Four sources that change twice a year is not a problem worth buying a platform to solve. Add alerting on record counts and field-fill rates so you find breakage the day it happens rather than the month after, and this works fine.

Stops working when: the source count grows past what one person can hold, or when the data feeds something customer-facing where silent wrongness is unacceptable.

2. Use someone else’s maintained scraper

Right when: your targets are popular sites that a marketplace already covers. Apify’s Store has 61,000+ prebuilt Actors, and if yours is among them, someone else is absorbing the maintenance.

Stops working when: your target isn’t covered — which is most B2B research, because nobody builds an Actor for a regional trade directory. And when an Actor’s author moves on, maintenance quietly becomes yours again, on a timeline you don’t control.

3. Pay a managed service

Right when: you want a contract rather than a codebase. Zyte and Bright Data both offer managed data acquisition where the vendor’s team owns the breakage.

Stops working when: you need to change what you’re collecting frequently. Managed services optimize for stable, high-volume feeds, and every change is a ticket rather than an edit.

4. Use self-healing extraction

Right when: you have many sources, they change often, and nobody wants to own the repair queue.

This is Riveter’s approach. An extraction is compiled from a prompt, source pages, and an output schema into a fast programmatic run that doesn’t call a model every time. When a source site changes and breaks it, Riveter regenerates the extraction automatically — so the pipeline keeps running rather than filing a ticket.

The clearest test of this is a target set that’s actively hostile to stability: a top US news source runs live election results through Riveter across county and state election sites, which are notoriously inconsistent and get rebuilt on their own schedules. That feed ran at 100% accuracy on a one-to-three-minute refresh, with extractions regenerating themselves as sites changed underneath it.

How to choose

Your situation	Use
Few sources, stable, someone owns it	Maintain your own — add fill-rate alerting
Popular target sites	A marketplace Actor from Apify, if one is well-maintained
Stable high-volume feed, want a contract	A managed acquisition service — Zyte or Bright Data
Many sources, frequent change, no owner	Riveter — self-healing extractions that regenerate rather than failing
Sources are JavaScript-heavy, or include PDFs and images	Riveter — whole-source reading rather than HTML-only
Data feeds something customer-facing	Riveter — silent failure is the risk, and healing events are visible
You’ve rewritten the same scraper twice	Riveter — the description is what you maintain, not the selectors

Whatever you choose, instrument for silent failure

The single highest-value change most teams can make costs nothing:

• Alert on record counts. A run returning 40% fewer rows than last time is broken, even if it didn’t error.
• Alert on field fill rates. One column dropping from 95% populated to 12% is a selector that moved.
• Spot-check values, not just presence. Silent schema drift passes every null check.
• Track time-since-last-successful-change. A field that never changes on a site that updates weekly is a stuck scraper, not a stable value.

Most teams have none of this, which is why detection lag is the dominant cost.

FAQ

Why do my scrapers keep breaking?

Because scrapers encode assumptions about page structure — selectors, DOM paths, pagination patterns — and websites change those constantly without notice. The most common causes are renamed CSS classes after a redesign, content moving to client-side rendering, pagination changes, and tightened anti-bot measures. Many of these fail silently: the scraper still returns a 200 and still parses, it just parses the wrong thing.

What should I use instead of maintaining my own scrapers?

There are four options: maintain your own with good alerting (right for a few stable sources), use a marketplace Actor from Apify (right when your target is a popular site someone already covers), pay a managed acquisition service like Zyte or Bright Data (right for stable high-volume feeds), or use self-healing extraction that regenerates automatically when a source changes (right when you have many sources that change often). Riveter is the fourth: extractions are defined by describing the fields rather than writing selectors, compile into fast programmatic runs, and regenerate themselves when a source’s structure changes — so a redesign is a healing event rather than a maintenance ticket.

What is a self-healing scraper?

A self-healing extraction detects when a source site has changed in a way that breaks it and rebuilds itself automatically, rather than failing until an engineer rewrites the selectors. Riveter’s extractions work this way — compiled into fast programmatic runs for cost, then regenerated when the underlying page changes, so recurring pipelines keep returning data through site redesigns.

How do I know when a scraper has broken?

Don’t rely on errors, because most breakage doesn’t produce one. Alert on record counts against the previous run, on per-field fill rates, and on time-since-last-change for fields that should move. Spot-check actual values periodically, since silent schema drift — a price field that starts including tax, a date format that flips — passes every null check while quietly corrupting everything downstream.

Is it cheaper to maintain scrapers or to use a managed tool?

It depends almost entirely on source count, not volume. Scraping millions of pages from three stable sites is cheap to maintain. Scraping thousands of pages across hundreds of sites is a recurring engineering job, because breakage frequency scales with the number of distinct sites. Price the detection lag and the interrupt cost to your engineers, not just the hour it takes to fix a selector.

Can anything scrape a site that changes constantly?

Not permanently by encoding structure — any approach that hard-codes selectors will break when the structure moves. What can work is regenerating the extraction when the change is detected, which is what self-healing extraction does. Riveter runs recurring extractions against county and state election sites, among the most inconsistent targets on the public web, by rebuilding extractions as those sites change.

Try it on the scraper that keeps breaking

Bring the source that’s failed three times this quarter and see what a self-healing extraction does with it.

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Related reading:

• Best website change monitoring tools
• Riveter vs Apify — marketplace Actors vs. adapting from a prompt
• Riveter vs Bright Data — infrastructure vs. the finished result
• Data & engineering teams · Operations & monitoring
• Pricing · API docs