Every engineering team that collects web data faces the same decision: build it in-house or use a service. Most start by building. It seems straightforward: write a script, deploy it, done.
Six months later, that script is a full-time job.
The Maintenance Tax
A 2025 Zyte industry report found that maintaining web scrapers consumes an average of 40% of a data team's time. Not building new features. Not analyzing data. Just keeping existing scrapers alive.
Here's where the time goes:
Site Layout Changes
Websites redesign constantly. When a target site moves a price element from div.price to span.product-price, your scraper returns empty data until someone notices and updates the selector. For teams tracking hundreds of sites, layout changes happen weekly.
Anti-Bot Updates
Cloudflare, DataDome, and Akamai update their detection systems regularly. A scraper that worked yesterday returns captcha pages today. Fixing this requires proxy rotation, updates to the request signature, or switching to full browser rendering, each with its own complexity.
Infrastructure Scaling
Browser-based scraping is resource-intensive. A single headless browser instance uses 200-500MB of RAM. Scaling to hundreds of concurrent pages means managing browser pools, dealing with memory leaks, and handling zombie processes.
IP Management
Maintaining a proxy pool means dealing with IP bans, monitoring proxy health, rotating between providers, and managing the cost of residential vs. data center proxies.
The Real Cost
Consider a mid-size e-commerce company tracking 500 competitor product pages across 20 sites:
In-house approach:
- 1 senior engineer: ~20% of their time on scraper maintenance = ~$30K/year equivalent
- Proxy costs: $200-500/month = $2,400-6,000/year
- Infrastructure (servers, browsers): $100-300/month = $1,200-3,600/year
- Downtime and data gaps: difficult to quantify, but always more than zero
Total: $33,600-39,600/year, plus the opportunity cost of engineering time that could be spent on core product features.
A scraping API handles all of this for a fraction of the cost and frees the engineering team to work on what actually differentiates the business: analyzing and acting on the data.
When In-House Makes Sense
Building your own scrapers is the right choice when:
- You have highly custom extraction logic that changes frequently
- Data volume is massive (millions of pages daily)
- You need full control over the scraping pipeline for compliance reasons
- You have a dedicated data engineering team with spare capacity
For everyone else, the math favors an API.
The Trend Line
The web scraping market is projected to grow from $1.17 billion to $2.28 billion by 2030 according to Research and Markets. That growth is driven largely by companies making the build-vs-buy calculation and choosing to buy.
And honestly, the complexity of web data collection is increasing faster than most teams can keep up with. The 40% maintenance tax from Zyte's report? That number is only going up as anti-bot systems get smarter. Teams that recognized this early and moved to APIs aren't just saving money. They're shipping product features while their competitors are still debugging proxy rotations.
Sources: Zyte State of Web Scraping 2025, Research and Markets Web Scraping Market Report 2026