How to Scrape Flipkart Data: Complete Guide for 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Scrape Flipkart product pages using AlterLab's API with automatic anti-bot handling. Start with T1/T2 tiers for static content, escalate to T3 for JS-protected pages. Extract structured data via CSS selectors or Cortex AI for typed JSON output. Costs begin at $0.0002/request.

Why collect e-commerce data from Flipkart?

Flipkart hosts over 150 million products across categories like electronics, fashion, and home goods. Engineering teams scrape this public data for:

• Price intelligence: Track competitor pricing fluctuations for dynamic pricing models
• Market research: Analyze product availability, category trends, and seasonal demand patterns
• Data enrichment: Enhance internal catalogs with standardized product attributes from public listings

Technical challenges

Flipkart employs layered anti-bot protections common to major e-commerce sites:

• Rate limiting based on IP and request patterns
• Header validation (User-Agent, Accept, Referer checks)
• Occasional JavaScript rendering requirements for dynamic content
• Bot detection via behavioral analysis and fingerprinting

Raw HTTP requests frequently receive 403/429 responses or altered HTML. AlterLab's Smart Rendering API manages these challenges through:

• Automatic proxy rotation with residential IPs
• Realistic browser fingerprinting
• Header normalization and cookie handling
• Tiered rendering escalation (curl → browser) without code changes

Quick start with AlterLab API

Begin by installing the SDK and making your first request. See the Getting started guide for detailed setup.

Python example

from alterlab import AlterLabSync

client = AlterLabSync("YOUR_API_KEY")
response = client.scrape("https://www.flipkart.com/apple-iphone-15-pro-max-black-titanium-256-gb/p/itmdc5308fa78822")
print(response["content"][:500])  # First 500 chars of HTML

Node.js example

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape({ url: "https://www.flipkart.com/apple-iphone-15-pro-max-black-titanium-256-gb/p/itmdc5308fa78822" });
console.log(response.text.slice(0, 500));

cURL example

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.flipkart.com/apple-iphone-15-pro-max-black-titanium-256-gb/p/itmdc5308fa78822"}'

Note: Flipkart product pages typically succeed at T2/T3 tiers. The API auto-escalates if initial attempts fail—you only pay for the successful tier.

Extracting structured data

Parse relevant data points using CSS selectors in your post-processing layer. Common Flipkart selectors:

Extract these in Python:

from parsel import Selector

selector = Selector(text=response.text)
data = {
    "title": selector.css("h1 span.B_NuCI::text").get(),
    "price": selector.css("div._30jeq3._16Jk6d::text").get(),
    "rating": selector.css("div._3LWZlK._1BLPMq::text").get(),
    "in_stock": "In Stock" in selector.css("div._16FRp0::text").get()
}

Structured JSON extraction with Cortex

For guaranteed typed output without selector maintenance, use AlterLab's Cortex AI extraction. Define a JSON schema and receive validated data:

from alterlab import AlterLabSync

client = AlterLabSync("YOUR_API_KEY")
result = client.scrape(
    url="https://www.flipkart.com/apple-iphone-15-pro-max-black-titanium-256-gb/p/itmdc5308fa78822",
    extraction_schema={
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "price": {"type": "number"},  # Converted from string like "₹1,44,900" → 144900
            "rating": {"type": "number"},
            "availability": {"type": "string"},
            "brand": {"type": "string"},
            "storage": {"type": "string"}
        },
        "required": ["title", "price"]
    }
)
print(result["filtered_content"])
# Output: {"title": "Apple iPhone 15 Pro Max", "price": 144900, "rating": 4.5, ...}

Cortex handles:

• Currency/number parsing
• Missing field normalization
• Schema validation and type coercion
• Fallback to traditional selectors when AI confidence is low

Cost breakdown

Flipkart's anti-bot landscape typically requires T2-T3 tiers. AlterLab auto-escalates from T1—you pay only for the tier that successfully retrieves data.

View detailed pricing including volume discounts. Example monthly cost for 100K Flipkart product scrapes (avg T3): $200.

Best practices

• Rate limiting: Start with 1 request/second per IP, adjust based on response headers
• Robots.txt compliance: Check https://www.flipkart.com/robots.txt for disallowed paths
• Error handling: Implement