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, TLS fingerprint updates, or switching to full browser rendering, each with its own complexity.
Infrastructure Scaling
Browser-based scraping is resource-intensive. A single headless Chrome instance uses 200-500MB of RAM. Scaling to hundreds of concurrent pages means managing Chrome 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