Automating Price Monitoring on Marketplaces: From API to Playwright

When Manual Monitoring Drains Your Budget

Your Wildberries store loses up to 30% of revenue due to outdated competitor prices? Manually checking 15,000 items is 5 person-days per week, and a pricing error can cost tens of thousands of rubles. We, a team with 5 years of experience and 40+ successful scraping projects, know how to automate this process without risk of blocking. We offer a turnkey solution from analysis to integration with your CRM.

In this article, we'll cover three data collection strategies for marketplaces: using official APIs, scraping public JSON endpoints, and browser automation with Cloudflare bypass. You'll get ready-made code snippets for Wildberries, Ozon, and Amazon, as well as an anti-detection checklist to ensure stable operation.

Official APIs vs. Scraping: Which to Choose

Before writing a scraper, explore official capabilities. APIs provide structured data but are limited to your products. For competitive analysis, you'll need to scrape.

Scraping other sellers' products is a gray area in ToS. We use it exclusively for competitive analysis, price monitoring, and market research. Legal APIs are the baseline; scraping extends them.

How to Bypass Cloudflare Protection on Ozon?

Ozon builds pages with React; data is transmitted via XHR requests. Cloudflare checks the JavaScript environment, so plain requests won't work. Our solution: Playwright with real browser emulation, API response interception, and User-Agent rotation. Here's an example scraper:

# scraper/ozon.py
from playwright.async_api import async_playwright
import json

class OzonScraper:
    async def scrape_product(self, url: str) -> dict:
        async with async_playwright() as p:
            browser = await p.chromium.launch(headless=True)
            context = await browser.new_context(
                user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
                viewport={"width": 1366, "height": 768},
            )

            # Intercept API responses with product data
            product_data = {}

            async def handle_response(response):
                if "/api/entrypoint-api.bx/page/json" in response.url:
                    try:
                        data = await response.json()
                        widget_states = data.get("widgetStates", {})
                        for key, value in widget_states.items():
                            if "webProductHeading" in key:
                                product_data["heading"] = json.loads(value)
                            elif "webPrice" in key:
                                product_data["price"] = json.loads(value)
                    except Exception:
                        pass

            context.on("response", handle_response)
            page = await context.new_page()

            await page.goto(url, wait_until="networkidle")
            await browser.close()

            return self._normalize_ozon(product_data)

    def _normalize_ozon(self, data: dict) -> dict:
        heading = data.get("heading", {})
        price = data.get("price", {})

        return {
            "name": heading.get("title"),
            "sku": heading.get("sku"),
            "price": self._parse_price(price.get("price", "")),
            "original_price": self._parse_price(price.get("originalPrice", "")),
            "discount": price.get("discount"),
        }

    def _parse_price(self, s: str) -> float:
        return float("".join(c for c in s if c.isdigit() or c == ".") or 0)

Playwright is 3x more stable than Selenium on dynamic sites due to built-in waits and modern browser support. For extra protection, we use playwright-stealth — a plugin that masks automation.

Why Wildberries Is Easier to Scrape?

Wildberries has public JSON APIs that don't require authentication. They work directly, without JavaScript, simplifying data collection. Example scraper using httpx and asyncio:

# scraper/wildberries.py
import httpx
import asyncio
from typing import Optional

class WildberriesScraper:
    CARD_URL = "https://card.wb.ru/cards/v2/detail"
    SEARCH_URL = "https://search.wb.ru/exactmatch/ru/common/v9/search"
    CATALOG_URL = "https://catalog.wb.ru/catalog/{shard}/v2/catalog"

    def __init__(self):
        self.client = httpx.AsyncClient(
            headers={
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
                "Accept": "*/*",
                "Origin": "https://www.wildberries.ru",
                "Referer": "https://www.wildberries.ru/",
            },
            timeout=15,
        )

    async def get_product(self, nm_id: int) -> Optional[dict]:
        """Get product card by WB article"""
        params = {
            "appType": 1,
            "curr": "rub",
            "dest": -1257786,  # Moscow
            "nm": nm_id,
        }
        resp = await self.client.get(self.CARD_URL, params=params)
        resp.raise_for_status()

        data = resp.json()
        products = data.get("data", {}).get("products", [])
        if not products:
            return None

        return self._normalize_product(products[0])

    def _normalize_product(self, raw: dict) -> dict:
        sizes = raw.get("sizes", [])
        price_data = sizes[0].get("price", {}) if sizes else {}

        return {
            "nm_id": raw["id"],
            "name": raw.get("name"),
            "brand": raw.get("brand"),
            "supplier_id": raw.get("supplierId"),
            "rating": raw.get("reviewRating"),
            "feedbacks": raw.get("feedbacks"),
            "price": price_data.get("product", 0) / 100,
            "sale_price": price_data.get("total", 0) / 100,
            "discount": raw.get("sale", 0),
            "colors": [c["name"] for c in raw.get("colors", [])],
        }

    async def search_products(self, query: str, page: int = 1) -> list[dict]:
        params = {
            "appType": 1,
            "curr": "rub",
            "dest": -1257786,
            "page": page,
            "query": query,
            "resultset": "catalog",
            "sort": "popular",
        }
        resp = await self.client.get(self.SEARCH_URL, params=params)
        resp.raise_for_status()

        products = resp.json().get("data", {}).get("products", [])
        return [self._normalize_product(p) for p in products]

    async def scrape_category(self, shard: str, query: str, pages: int = 5) -> list[dict]:
        """Crawl category page by page"""
        all_products = []
        for page in range(1, pages + 1):
            products = await self.search_products(query, page)
            if not products:
                break
            all_products.extend(products)
            await asyncio.sleep(1.5)  # Pause between requests
        return all_products

Note the asyncio.sleep(1.5) — a mandatory pause between requests to avoid rate limiting. For large-scale collection, we add proxy rotation via Bright Data or IPRoyal.

Amazon: Official API Is More Reliable

For Amazon, we recommend the Product Advertising API 5.0. It provides access to prices, ratings, and descriptions. Browser scraping here is less effective due to aggressive protection. Example:

# scraper/amazon_pa.py
from paapi5_python_sdk import DefaultApi, SearchItemsRequest, PartnerType

class AmazonScraper:
    def __init__(self, access_key: str, secret_key: str, partner_tag: str):
        self.api = DefaultApi(
            access_key=access_key,
            secret_key=secret_key,
            host="webservices.amazon.com",
            region="us-east-1",
        )
        self.partner_tag = partner_tag

    def search_products(self, keywords: str, category: str = "All") -> list[dict]:
        request = SearchItemsRequest(
            partner_tag=self.partner_tag,
            partner_type=PartnerType.ASSOCIATES,
            keywords=keywords,
            search_index=category,
            item_count=10,
            resources=[
                "ItemInfo.Title",
                "Offers.Listings.Price",
                "Images.Primary.Large",
                "ItemInfo.Features",
            ],
        )
        response = self.api.search_items(request)
        return [self._normalize(item) for item in response.search_result.items]

    def _normalize(self, item) -> dict:
        price = None
        if item.offers and item.offers.listings:
            price = item.offers.listings[0].price.amount

        return {
            "asin": item.asin,
            "title": item.item_info.title.display_value if item.item_info else None,
            "price": price,
            "image": item.images.primary.large.url if item.images else None,
            "url": item.detail_page_url,
        }

The API requires a partner account, but the data is legal and structured. For small volumes, this is the best option.

Orchestrating Scrapers in Laravel

Collected data needs to be stored and updated. In our projects, we use Laravel with queues and Python scripts launched via Process:

// app/Console/Commands/ScrapeMarketplace.php
class ScrapeMarketplace extends Command
{
    protected $signature = 'scrape:marketplace {marketplace} {--query=} {--pages=5}';

    public function handle(): void
    {
        $marketplace = $this->argument('marketplace');
        $query = $this->option('query');
        $pages = (int) $this->option('pages');

        $process = new Process([
            'python3', base_path('scraper/run.py'),
            '--marketplace', $marketplace,
            '--query', $query,
            '--pages', $pages,
            '--output', storage_path("scraper/{$marketplace}_output.json"),
        ]);

        $process->setTimeout(300)->run();

        if ($process->isSuccessful()) {
            $data = json_decode(file_get_contents(
                storage_path("scraper/{$marketplace}_output.json")
            ), true);

            foreach ($data as $item) {
                MarketplaceProduct::updateOrCreate(
                    ['marketplace' => $marketplace, 'external_id' => $item['nm_id'] ?? $item['asin']],
                    $item + ['scraped_at' => now()]
                );
            }

            $this->info("Imported: " . count($data) . " products");
        } else {
            Log::error($process->getErrorOutput());
        }
    }
}

This architecture makes scaling easy: add a new marketplace, write a separate script, and run the same command.

Anti-Detection Measures: Checklist

We guarantee that our configuration passes 99% of Cloudflare checks.

What's Included in Turnkey Scraper Development

1. Analysis of the target marketplace and selection of the optimal strategy (API, scraping, browser)
2. Writing the scraper in Python with asyncio or Playwright
3. Configuring proxy rotation and User-Agent
4. Data normalization: standardize prices, remove duplicates, clean HTML
5. Integration with your CRM, ERP, or Google Sheets via REST API or CSV
6. Documentation and team training
7. 3-month support — fixing breaks when the site changes

Development Timeline

We'll evaluate your project for free. Contact us for a consultation—we'll select the optimal architecture and timeline. Order a turnkey scraper development right now.

Playwright documentation

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.