Calculate the true cost of scraping with proxies versus using a search API by accounting for proxy fees, bandwidth, CAPTCHA solving, failure retry costs, and developer maintenance hours. Most teams underestimate scraping costs by 3-5x because they only count the proxy subscription. The hidden costs are retry bandwidth for failed requests, CAPTCHA solving services, developer time maintaining selectors, and infrastructure for headless browsers. This tutorial builds a cost calculator that compares both approaches honestly.
Prerequisites
- Python 3.8+ installed
- Current proxy provider pricing
- A Scavio API key from scavio.dev for API cost comparison
- Monthly request volume estimate
Walkthrough
Step 1: Count your requests
Audit your actual request volume including retries, failures, and CAPTCHA hits.
Python
import json
# Request volume audit
def audit_volume(monthly_target: int, failure_rate: float = 0.15, captcha_rate: float = 0.05) -> dict:
actual_requests = monthly_target / (1 - failure_rate) # Account for retries
captcha_requests = monthly_target * captcha_rate
total = actual_requests + captcha_requests
return {
'target_successful': monthly_target,
'with_retries': int(actual_requests),
'captcha_solves': int(captcha_requests),
'total_requests': int(total),
'overhead_pct': round((total / monthly_target - 1) * 100, 1),
}
volume = audit_volume(100000)
for k, v in volume.items():
print(f'{k}: {v}')
print(f'\nYou need {volume["total_requests"]} total requests for {volume["target_successful"]} successful results')Step 2: Calculate proxy cost with all features
Calculate the full proxy scraping cost including subscription, bandwidth, CAPTCHA solving, and infrastructure.
Python
def proxy_cost(volume: dict, proxy_per_gb: float = 12.0, avg_page_kb: int = 200,
captcha_per_solve: float = 0.003, server_monthly: float = 50) -> dict:
bandwidth_gb = (volume['total_requests'] * avg_page_kb) / (1024 * 1024)
proxy_fee = bandwidth_gb * proxy_per_gb
captcha_fee = volume['captcha_solves'] * captcha_per_solve
# Headless browser infra (Chrome instances)
infra = server_monthly
# Total
total = proxy_fee + captcha_fee + infra
return {
'bandwidth_gb': round(bandwidth_gb, 1),
'proxy_fee': round(proxy_fee, 2),
'captcha_fee': round(captcha_fee, 2),
'infrastructure': server_monthly,
'total_monthly': round(total, 2),
'per_request': round(total / volume['target_successful'], 5),
}
proxy = proxy_cost(volume)
print('PROXY SCRAPING COSTS:')
for k, v in proxy.items():
print(f' {k}: ${v}' if isinstance(v, (int, float)) else f' {k}: {v}')Step 3: Calculate API cost
Calculate the Scavio API cost for the same volume. No retries, CAPTCHAs, or infrastructure needed.
Python
def api_cost(target_successful: int, cost_per_credit: float = 0.005) -> dict:
# API calls = target (no retries needed, no CAPTCHAs)
total = target_successful * cost_per_credit
return {
'api_calls': target_successful,
'cost_per_credit': cost_per_credit,
'total_monthly': round(total, 2),
'per_request': cost_per_credit,
'retries_needed': 0,
'captcha_solves': 0,
'infrastructure': 0,
}
api = api_cost(volume['target_successful'])
print('API COSTS:')
for k, v in api.items():
print(f' {k}: ${v}' if isinstance(v, (int, float)) else f' {k}: {v}')Step 4: Add maintenance hours
Factor in developer time for maintaining scrapers versus zero maintenance for an API.
Python
def maintenance_cost(dev_hourly: float = 75, scraper_hours_monthly: float = 8, api_hours_monthly: float = 0.5) -> dict:
scraper_maint = dev_hourly * scraper_hours_monthly
api_maint = dev_hourly * api_hours_monthly
return {
'scraper_dev_hours': scraper_hours_monthly,
'scraper_dev_cost': scraper_maint,
'api_dev_hours': api_hours_monthly,
'api_dev_cost': api_maint,
'savings': round(scraper_maint - api_maint, 2),
}
maint = maintenance_cost()
print('MAINTENANCE COSTS:')
for k, v in maint.items():
print(f' {k}: ${v}' if isinstance(v, (int, float)) else f' {k}: {v}')Step 5: Compare total cost
Sum all costs and produce a side-by-side comparison report.
Python
def full_comparison(monthly_target: int = 100000) -> dict:
vol = audit_volume(monthly_target)
p = proxy_cost(vol)
a = api_cost(monthly_target)
m = maintenance_cost()
proxy_total = p['total_monthly'] + m['scraper_dev_cost']
api_total = a['total_monthly'] + m['api_dev_cost']
savings = proxy_total - api_total
print(f'\n{"=" * 50}')
print(f'COST COMPARISON: {monthly_target:,} searches/month')
print(f'{"=" * 50}')
print(f'{"":<25} {"Proxy Scraping":<18} {"Search API"}')
print(f'{"API/proxy fees":<25} ${p["total_monthly"]:<17.2f} ${a["total_monthly"]:.2f}')
print(f'{"Dev maintenance":<25} ${m["scraper_dev_cost"]:<17.2f} ${m["api_dev_cost"]:.2f}')
print(f'{"TOTAL":<25} ${proxy_total:<17.2f} ${api_total:.2f}')
print(f'{"Per request":<25} ${p["per_request"]:<17.5f} ${a["per_request"]:.5f}')
print(f'\nMonthly savings with API: ${savings:.2f}')
print(f'Annual savings: ${savings * 12:.2f}')
return {'proxy_total': proxy_total, 'api_total': api_total, 'savings': savings}
full_comparison(100000)Python Example
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def cost_per_search():
# API: $0.005/credit, no retries, no proxy, no CAPTCHAs
api_cost = 0.005
# Proxy: ~$0.008-0.015 after retries + CAPTCHA + infra
proxy_cost = 0.012
savings_pct = round((1 - api_cost / proxy_cost) * 100)
print(f'API: ${api_cost}/search, Proxy: ~${proxy_cost}/search')
print(f'API is {savings_pct}% cheaper')
cost_per_search()JavaScript Example
JavaScript
// Cost comparison calculator
function compareCosts(monthlySearches) {
const apiCost = monthlySearches * 0.005;
const proxyCost = monthlySearches * 0.012; // After retries, CAPTCHA, infra
const savings = proxyCost - apiCost;
console.log(`API: $${apiCost.toFixed(2)}/mo`);
console.log(`Proxy: $${proxyCost.toFixed(2)}/mo`);
console.log(`Savings: $${savings.toFixed(2)}/mo`);
}
compareCosts(100000);Expected Output
JSON
A side-by-side cost comparison showing proxy scraping vs API costs including hidden expenses: retries, CAPTCHAs, infrastructure, and developer maintenance time.