Real-time Farfetch price tracking - a global luxury fashion online retail platform. Two ways to get started: a fully managed intelligence platform or a custom scraper built with Bright Data's AI Scraper Builder.

Bright Insights is Bright Data's fully managed retail intelligence platform. No scrapers to build, no infrastructure to maintain - just structured, analysis-ready price data delivered to dashboards, data feeds, or your BI tools.

Why teams choose Bright Insights:

• 🚀 Zero setup - Go live in minutes with ready-to-use dashboards and data feeds
• 🤖 AI-powered recommendations - A conversational AI assistant turns millions of data points into actionable insights instantly
• ⚡ Real-time monitoring - Hourly to daily refresh rates with instant alerts (email, Slack, webhook)
• 🌍 Unlimited scale - Any website, any geography, any refresh frequency
• 🔗 Plug-and-play integrations - AWS, GCP, Databricks, Snowflake, and more
• 🛡️ Fully managed - Bright Data handles schema changes, site updates, and data quality automatically

Key use cases:

• ✅ Monitor rare and limited-edition pricing on Farfetch
• ✅ Track availability across sizes and colours in real time
• ✅ Verify resale premiums vs. retail pricing
• ✅ Monitor MAP policy compliance and detect pricing violations
• ✅ Track competitor promotions and promotional dynamics
• ✅ Feed clean, harmonized data directly into dynamic pricing algorithms or AI models

Starting from $250/month - Get a tailored quote →

No pre-built Farfetch scraper API? No problem. Bright Data's AI Scraper Builder generates a custom Farfetch scraper in just a few clicks — no coding required.

Open the Farfetch AI Scraper Builder →

Choose the domain, outline your data requirements, and let our AI scraper builder automatically create the API.

1. Describe data needs in plain English
2. AI instantly generates the scraper API
3. Run API requests for immediate results
4. Edit the code in the built-in IDE, if needed

Once built, your scraper gets a Web Scraper ID (gd_xxxxxxxxxxxx) — copy it for the Setup step below.

• Python 3.9 or higher
• A Bright Data account (free trial available)
• A Bright Data API token (how to get one)
• A Web Scraper ID for Farfetch (from the build step above)

1. Clone this repository

   git clone https://github.com/luminati-io/farfetch-price-tracker.git
   cd farfetch-price-tracker

2. Install dependencies

   pip install -r requirements.txt

3. Configure credentials

   Copy .env.example to .env and fill in your values:

   cp .env.example .env

   BRIGHTDATA_API_TOKEN=your_api_token_here
   BRIGHTDATA_DATASET_ID=your_dataset_id_here

   Your Web Scraper ID Paste the Web Scraper ID from your AI Scraper Builder dashboard into BRIGHTDATA_DATASET_ID (format: gd_xxxxxxxxxxxx).

Once your Farfetch scraper is built and your Web Scraper ID is configured in .env, the Python interface works the same way:

Pass a list of Farfetch product URLs to retrieve structured price data:

from price_tracker import track_prices

urls = [
    "https://www.farfetch.com/en/products/sample-product-123456",
    # Add more product URLs here
]

results = track_prices(urls)
for item in results:
    print(f"{item.get('title')} - {item.get('final_price', item.get('price'))} {item.get('currency', '')}")

Or run directly:

python price_tracker.py

Find products matching a keyword search:

from price_tracker import discover_by_keyword

results = discover_by_keyword("laptop", limit=50)

Collect all products from a Farfetch category page:

from price_tracker import discover_by_category

results = discover_by_category(
    "https://farfetch.com/category/example",
    limit=100,
)

Each result record contains the following fields:

[
  {
    "url": "https://www.farfetch.com/en/products/sample-product-123456",
    "title": "Example Product Name",
    "brand": "Example Brand",
    "initial_price": 59.99,
    "final_price": 44.99,
    "currency": "USD",
    "discount": "25%",
    "in_stock": true,
    "rating": 4.5,
    "reviews_count": 1234,
    "images": ["https://farfetch.com/images/product1.jpg"],
    "description": "Product description text...",
    "timestamp": "2025-01-15T10:30:00Z"
  }
]

The trigger_collection() function accepts optional parameters to control data collection:

Example with options:

from price_tracker import trigger_collection, get_results

inputs = [{"url": "https://www.farfetch.com/en/products/sample-product-123456"}]
snapshot_id = trigger_collection(inputs, limit=200, notify="https://your-webhook.com/hook")
results = get_results(snapshot_id)

• 🌟 Farfetch Price Tracker - Bright Insights (Managed)
• 🏗️ Build a Farfetch Scraper
• 📖 Bright Data Web Scraper API Documentation
• 🗄️ Web Scrapers Control Panel
• 🔑 How to get an API token
• 🌐 Bright Data Homepage

Built with Bright Data - the industry-leading web data platform.