Flipkart Data API: Extract Structured JSON in 2026

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

TL;DR

To get structured Flipkart data via API, use a schema-based extraction endpoint that converts public HTML into typed JSON. By sending a target URL and a JSON schema to the AlterLab Extract API, you can retrieve product titles, prices, and SKUs without writing custom CSS selectors or managing proxy rotation.

Why use Flipkart data?

E-commerce data is the foundation for several high-value engineering projects. Most developers aren't looking for raw HTML, but for clean datasets to power downstream applications.

• AI Training & RAG: Feed real-world product descriptions and specifications into Large Language Models (LLMs) to build specialized shopping assistants.
• Price Intelligence: Monitor price fluctuations across categories to automate competitive pricing strategies.
• Market Analytics: Analyze product trends, rating distributions, and availability to forecast demand in specific electronics or fashion segments.

What data can you extract?

When building a data pipeline for e-commerce, consistency is key. You should target publicly available fields that provide a complete picture of the product.

• Product Title: The full descriptive name of the item.
• Price & Currency: The current selling price and the currency symbol (e.g., ₹).
• SKU/Model Number: Unique identifiers used for inventory mapping.
• Availability: Status indicators (e.g., "In Stock", "Out of Stock").
• Ratings & Reviews: The average star rating and the total count of user reviews.
• Specifications: Technical tables containing brand, dimensions, and hardware specs.

The extraction approach

Traditional web scraping relies on BeautifulSoup or Playwright to find specific CSS selectors (e.g., .Nx_S_p or div._1AtVbE). This approach is fragile. E-commerce platforms frequently update their DOM structure, which breaks your parsers and creates maintenance overhead.

A data API approach shifts the burden from the developer to the platform. Instead of defining where the data is (selectors), you define what the data is (schema). By using an LLM-powered extraction layer, the API identifies the correct fields regardless of changes to the website's HTML layout.

Quick start with AlterLab Extract API

To begin, follow the Getting started guide to configure your environment. The Extract API allows you to pass a URL and a JSON schema to receive a validated response.

Python Implementation

Using the official SDK is the most efficient way to handle typed extraction.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "The title field"
    },
    "price": {
      "type": "string",
      "description": "The price field"
    },
    "currency": {
      "type": "string",
      "description": "The currency field"
    },
    "sku": {
      "type": "string",
      "description": "The sku field"
    },
    "availability": {
      "type": "string",
      "description": "The availability field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    }
  }
}

result = client.extract(
    url="https://flipkart.com/example-page",
    schema=schema,
)
print(result.data)

cURL Implementation

For lightweight integrations or shell scripts, use the REST endpoint as detailed in the Extract API docs.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://flipkart.com/example-page",
    "schema": {
      "properties": {
        "title": {"type": "string"}, 
        "price": {"type": "string"}, 
        "currency": {"type": "string"}
      }
    }
  }'

Define your schema

The power of a data API lies in the schema. AlterLab uses JSON Schema to validate the output. If the AI extracts a price as a string when you requested an integer, the platform handles the coercion or flags the error before the data hits your database.

Example Structured Output

When you call the API with the schema provided in the Python example, you receive a clean JSON object:

{
  "data": {
    "title": "Apple iPhone 15 (Black, 128 GB)",
    "price": "69,999",
    "currency": "INR",
    "sku": "APPLE2023iPhone15",
    "availability": "In Stock",
    "rating": "4.6"
  },
  "metadata": {
    "cost": "1200µ¢",
    "latency": "1.2s"
  }
}

Handle pagination and scale

Extracting a single product page is straightforward, but building a full catalog requires a different architecture.

Async Batching

For high-volume extraction, do not use synchronous requests. Use async jobs to submit multiple URLs and poll for results. This prevents your application from idling while the API handles JavaScript rendering and AI extraction.

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")
urls = ["https://flipkart.com/p1", "https://flipkart.com/p2", "https://flipkart.com/p3"]

async def run_pipeline():
    # Submit multiple extraction jobs
    jobs = [client.extract_async(url=u, schema=my_schema) for u in urls]
    results = await asyncio.gather(*jobs)
    
    for res in results:
        print(f"Extracted: {res.data['title']}")

asyncio.run(run_pipeline())

Cost Management

To maintain budget predictability, use the cost estimation endpoint before committing to a large batch. Costs are clamped between $0.001 and $0.50 per request. If you provide your own LLM key (BYOK), the orchestration fee is reduced to 300 µ¢. Detailed information on consumption can be found on the AlterLab pricing page.

Key takeaways

• Avoid Selectors: Use schema-based extraction to prevent pipeline breakage.
• Typed Data: Define your requirements in JSON Schema to ensure data integrity.
• Scale with Async: Use asynchronous jobs for catalog-scale extraction.
• Public Data Only: Ensure compliance by targeting public pages and respecting robots.txt.

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