Teams that deleted their scraping stack.

A11yWatch provides automated WCAG accessibility audits. To do that, it needs to crawl every page of a customer's website, render JavaScript, and feed the HTML into its analysis engine. That's a lot of crawling, and it has to happen on demand.

Running that in-house meant dedicated crawl servers, a headless browser fleet for JS rendering, and proxy rotation to avoid rate limits. Multiple microservices just to get the HTML before the actual accessibility work could start. The infrastructure bill for crawling alone was around $2,000/month.

Replacing the crawl layer with Spider collapsed all of that into API calls. No servers to provision, no browsers to manage, no proxies to rotate. The monthly cost for the same crawling workload: about $13. The engineering time freed up went back into the actual product. Better audits, not better scrapers.

This team builds an AI research assistant. It depends on fresh documentation from hundreds of technical sites. Their in-house setup was three Python microservices, a proxy provider, and an engineer spending roughly half their time on scraper maintenance.

When anti-bot protections changed on a handful of key sites, pages that used to work started failing 30% of the time. The search product served stale results to paying customers for two days before they patched it.

They migrated to Spider over a weekend. One endpoint replaced the three services. Pages that were failing now succeed consistently. The proxy provider contract ($2,400/month) was cancelled. Total pipeline runtime went from 6 hours to 15 minutes.

Their pricing team tracked competitors across 400+ sites. The previous scraping provider used credit multipliers: 1 credit for a basic page, up to 75 credits for anything behind Cloudflare or Akamai. Most e-commerce sites use one of those.

Monthly bills swung between $800 and $4,200 depending on how many target sites had active bot protection that week. Budgeting was impossible.

Spider charges the same rate regardless of site protection. Monthly cost went from an unpredictable ~$3,800 average to a flat $120. They used the savings to expand coverage from 400 to 2,000+ competitor sites.

Financial compliance requires tracking regulatory changes across government and authority websites. Most of these sites have no RSS feeds and update unpredictably. The team had four analysts manually checking 1,200 URLs weekly.

The process: visit each page, check for changes, copy relevant text into a spreadsheet, flag updates on Slack. A missed regulatory update once resulted in a $50K fine.

Spider now crawls every URL daily, extracts structured text, and fires a webhook when content changes. The analysts moved from manual checking to policy analysis.

Two SDRs spent half their day on manual prospect research: visiting company websites, copying contact info, pasting into Salesforce. Output: about 80 leads per day between them.

Third-party enrichment tools had stale data and required $15K/year contracts. Data quality was inconsistent.

They built a pipeline using Spider's AI extraction: pass in company URLs, get back structured JSON with contacts, tech stack, and company size. It runs overnight and writes directly to Salesforce. Cost per lead went from $1.20 to $0.02.

Each new client meant writing custom scrapers for that client's competitors. Different sites, different rendering requirements, different bot protections. Each client was a separate maintenance burden.

At 50 clients, the engineer maintaining scrapers couldn't keep up. More time was going into scraping maintenance than into the analysis product itself.

A single Spider integration replaced all per-client scrapers. New client onboarding dropped from 2-3 days to under an hour. Add competitor URLs to a config, Spider handles the crawling.