Multi-platform price monitoring compares the same product across Amazon and Google Shopping to find the best deal and detect price changes. Instead of building separate scrapers for each platform, the Scavio API provides both Amazon product data and Google Shopping results through a single endpoint. This tutorial builds a monitoring pipeline that tracks products across platforms, stores historical prices, and alerts when prices drop below a threshold. Each platform check costs $0.005.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A list of products to monitor
Walkthrough
Step 1: Search products on both platforms
Create functions to search Amazon and Google Shopping for the same product. Both use the Scavio search API with different platform parameters.
Python
import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']
ENDPOINT = 'https://api.scavio.dev/api/v1/search'
def search_amazon(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'platform': 'amazon', 'query': query, 'marketplace': 'US'})
return resp.json().get('products', [])
def search_google_shopping(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'type': 'shopping'})
return resp.json().get('shopping_results', [])Step 2: Normalize prices across platforms
Amazon and Google Shopping return prices in different formats. Normalize them to float values for comparison.
Python
import re
def parse_price(price_str) -> float:
if not price_str:
return 0.0
if isinstance(price_str, (int, float)):
return float(price_str)
cleaned = re.sub(r'[^0-9.]', '', str(price_str))
try:
return float(cleaned)
except ValueError:
return 0.0
def compare_prices(query: str) -> dict:
amazon = search_amazon(query)
google = search_google_shopping(query)
amazon_prices = [{'title': p.get('title', '')[:60], 'price': parse_price(p.get('price')),
'platform': 'amazon', 'link': p.get('link', '')}
for p in amazon if parse_price(p.get('price')) > 0]
google_prices = [{'title': p.get('title', '')[:60], 'price': parse_price(p.get('price')),
'platform': 'google_shopping', 'link': p.get('link', '')}
for p in google if parse_price(p.get('price')) > 0]
all_prices = sorted(amazon_prices + google_prices, key=lambda x: x['price'])
return {'query': query, 'total_listings': len(all_prices),
'cheapest': all_prices[0] if all_prices else None,
'all': all_prices[:10]}
result = compare_prices('Sony WH-1000XM5')
print(f'Cheapest: ${result["cheapest"]["price"]} on {result["cheapest"]["platform"]}')Step 3: Store price history in JSON
Save daily price snapshots so you can track trends and detect drops. Each product gets a time-series of prices per platform.
Python
import json
from datetime import date
HISTORY_FILE = 'price_history.json'
def load_history() -> dict:
try:
with open(HISTORY_FILE) as f:
return json.load(f)
except FileNotFoundError:
return {}
def save_snapshot(query: str, prices: list) -> None:
history = load_history()
today = date.today().isoformat()
if query not in history:
history[query] = []
history[query].append({
'date': today,
'prices': [{'platform': p['platform'], 'price': p['price'],
'title': p['title'][:40]} for p in prices[:5]]
})
with open(HISTORY_FILE, 'w') as f:
json.dump(history, f, indent=2)
# Save today's data:
result = compare_prices('Sony WH-1000XM5')
save_snapshot('Sony WH-1000XM5', result['all'])
print(f'Saved snapshot with {len(result["all"])} listings')Step 4: Detect price drops from history
Compare today's prices with the previous snapshot. Alert when a product drops below a threshold or shows a significant percentage decrease.
Python
def detect_drops(query: str, drop_threshold_pct: float = 10.0) -> list:
history = load_history()
snapshots = history.get(query, [])
if len(snapshots) < 2:
return []
prev = {p['title']: p['price'] for p in snapshots[-2]['prices']}
current = {p['title']: p['price'] for p in snapshots[-1]['prices']}
drops = []
for title, curr_price in current.items():
if title in prev and prev[title] > 0:
pct_change = ((curr_price - prev[title]) / prev[title]) * 100
if pct_change < -drop_threshold_pct:
drops.append({
'product': title,
'old_price': prev[title],
'new_price': curr_price,
'drop_pct': round(abs(pct_change), 1)
})
return drops
drops = detect_drops('Sony WH-1000XM5')
for d in drops:
print(f'PRICE DROP: {d["product"]} ${d["old_price"]} -> ${d["new_price"]} (-{d["drop_pct"]}%)')Step 5: Run daily monitoring for all products
Create a main script that monitors multiple products and reports all findings. Schedule it with cron for daily execution.
Python
def daily_monitor(products: list) -> None:
print(f'Price monitor: {len(products)} products, {date.today()}')
all_drops = []
for product in products:
result = compare_prices(product)
save_snapshot(product, result['all'])
cheapest = result.get('cheapest')
if cheapest:
print(f' {product}: ${cheapest["price"]} ({cheapest["platform"]})')
drops = detect_drops(product)
all_drops.extend(drops)
if all_drops:
print(f'\n{len(all_drops)} price drops detected:')
for d in all_drops:
print(f' {d["product"]}: ${d["old_price"]} -> ${d["new_price"]} (-{d["drop_pct"]}%)')
cost = len(products) * 2 * 0.005 # 2 API calls per product
print(f'\nAPI cost: ${cost:.2f} ({len(products) * 2} credits)')
if __name__ == '__main__':
daily_monitor(['Sony WH-1000XM5', 'Apple AirPods Pro', 'Samsung Galaxy Buds'])Python Example
Python
import os, requests, re, json
from datetime import date
API_KEY = os.environ['SCAVIO_API_KEY']
EP = 'https://api.scavio.dev/api/v1/search'
def search(body):
return requests.post(EP, headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'}, json=body).json()
def parse_price(p):
try: return float(re.sub(r'[^0-9.]', '', str(p or '0')))
except: return 0.0
def compare(query):
amazon = search({'platform': 'amazon', 'query': query, 'marketplace': 'US'}).get('products', [])
google = search({'query': query, 'country_code': 'us', 'type': 'shopping'}).get('shopping_results', [])
all_p = [{'title': p.get('title','')[:50], 'price': parse_price(p.get('price')), 'src': 'amazon'} for p in amazon]
all_p += [{'title': p.get('title','')[:50], 'price': parse_price(p.get('price')), 'src': 'google'} for p in google]
return sorted([p for p in all_p if p['price'] > 0], key=lambda x: x['price'])
for q in ['Sony WH-1000XM5', 'AirPods Pro']:
results = compare(q)
if results:
print(f'{q}: ${results[0]["price"]} ({results[0]["src"]})')JavaScript Example
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
const EP = 'https://api.scavio.dev/api/v1/search';
async function search(body) {
const r = await fetch(EP, {
method: 'POST',
headers: { 'x-api-key': API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify(body)
});
return r.json();
}
function parsePrice(p) {
return parseFloat(String(p || '0').replace(/[^0-9.]/g, '')) || 0;
}
async function compare(query) {
const [amazon, google] = await Promise.all([
search({ platform: 'amazon', query, marketplace: 'US' }),
search({ query, country_code: 'us', type: 'shopping' })
]);
const all = [
...(amazon.products || []).map(p => ({ title: p.title, price: parsePrice(p.price), src: 'amazon' })),
...(google.shopping_results || []).map(p => ({ title: p.title, price: parsePrice(p.price), src: 'google' }))
].filter(p => p.price > 0).sort((a, b) => a.price - b.price);
return all;
}
compare('Sony WH-1000XM5').then(r => {
if (r.length) console.log(`Cheapest: $${r[0].price} (${r[0].src})`);
});Expected Output
JSON
Price monitor: 3 products, 2026-05-13
Sony WH-1000XM5: $278.00 (amazon)
Apple AirPods Pro: $189.99 (google_shopping)
Samsung Galaxy Buds: $99.99 (amazon)
1 price drops detected:
Apple AirPods Pro: $199.99 -> $189.99 (-5.0%)
API cost: $0.03 (6 credits)