Shopee Data API: Extract Structured JSON in 2026

TL;DR

You can retrieve structured Shopee data as typed JSON by posting a URL and schema to AlterLab’s Extract API. The service validates output, handles anti‑bot bypass, and returns a predictable JSON payload ready for pipelines.

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

Why use Shopee data?

E‑commerce teams need fresh product information for multiple purposes. Common use cases include:

• Training machine‑learning models on real‑time pricing trends.
• Building competitive intelligence dashboards that monitor competitor catalogs.
• Feeding analytics pipelines that aggregate inventory levels across categories.

Because the data is publicly listed, it can be collected without authentication, but the volume and variability of Shopee’s pages make a reliable extraction method essential.

What data can you extract?

Shopee exposes several fields that are safe to scrape when they appear in the public product card. Typical fields include:

• title – the human‑readable product name.
• price – the numeric price value.
• currency – the three‑letter currency code (e.g., USD, SGD).
• sku – the stock‑keeping unit identifier used by Shopee.
• availability – stock status such as "In stock" or "Out of stock".
• rating – average customer rating, often shown as a star count.

All of these appear in the HTML of a product detail page and are safe to collect as long as you respect Shopee’s robots.txt and rate limits.

The extraction approach

Raw HTTP requests followed by CSS selectors are fragile. Site redesigns, dynamic JavaScript rendering, and anti‑bot defenses can break a scraper overnight. A modern data API solves these problems by:

1. Providing a stable endpoint that abstracts away HTTP details.
2. Offering automatic anti‑bot bypass and rotating proxies.
3. Returning validated, typed JSON instead of raw HTML.

With a data API you spend time building logic for your application, not debugging HTML changes.

Quick start with AlterLab Extract API

AlterLab lets you call /v1/extract to receive structured data in a single request. Below are minimal examples in Python and cURL.

from alterlab import AlterLabSync

client = AlterLabSync("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.scrape(
    url="https://shopee.com/example-page",
    extraction_schema=schema,
)
print(result["filtered_content"])

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

The response body is a JSON object that matches the schema you supplied, eliminating the need for post‑processing. For full documentation see the Extract API reference. Beginners can follow the Getting started guide to install the SDK and set up API keys.

Batch and async usage

When you need to harvest hundreds of product pages, sending requests sequentially hits rate limits. AlterLab supports asynchronous batches:

import asyncio
from alterlab import AlterLab

async def extract_product(url):
    schema = {"properties": {"title": {"type": "string"}, "price": {"type": "string"}}}
    return await AlterLab("YOUR_API_KEY").extract(url=url, schema=schema)

async def main():
    urls = ["https://shopee.com/p/123", "https://shopee.com/p/456", "https://shopee.com/p/789"]
    tasks = [extract_product(u) for u in urls]
    results = await asyncio.gather(*tasks)
    for r in results:
        print(r.data)

asyncio.run(main())

This pattern scales horizontally, respects built‑in throttling, and lets you process results as they arrive.

Define your schema

The schema is a JSON object that describes the fields you expect. AlterLab validates the extracted payload against this schema and returns only the fields you declared. Here’s a concise example for a Shopee product:

{
  "title": {"type": "string"},
  "price": {"type": "string"},
  "currency": {"type": "string"},
  "sku": {"type": "string"},
  "availability": {"type": "string"},
  "rating": {"type": "string"}
}

When you submit this schema, the API guarantees that the data field in the response contains exactly those keys with correctly typed values. This eliminates manual parsing and reduces errors in downstream pipelines.

Handle pagination and scale

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