PriceGrabber Data API: Extract Structured JSON in 2026

Extract publicly listed price‑comparison data from PriceGrabber using AlterLab’s Extract API. Send a URL and a JSON schema, receive typed output, and preview cost before execution.

Why use PriceGrabber data?

• AI training datasets that need up‑to‑date product pricing.
• Competitive analytics that compare retail prices across merchants.
• Market research pipelines that aggregate availability and stock information.

All of these use cases rely on publicly accessible pages, not on logged‑in or paywalled content. The data is limited to product names, prices, store identifiers, availability signals, and the source URL.

What data can you extract?

The following fields are commonly available on PriceGrabber listings and can be requested as typed JSON:

• product_name – the human readable name of the item
• price – the listed price string, often with currency symbols
• store – the retailer or marketplace name
• availability – a status flag such as in_stock or out_of_stock
• url – the direct link to the product page

The schema you define determines which of these fields are returned, and AlterLab validates the output against the schema before delivering it.

The extraction approach

Scraping raw HTML with a curl command or a headless browser is fragile. Site layout changes break selectors, and you must manage proxy rotation, captcha solving, and request throttling yourself. A dedicated data API abstracts those concerns and provides a stable contract: you specify what you need, and the service returns it in a predictable format.

AlterLab’s Extract API does exactly that. It handles anti‑bot bypass, rotating proxies, and rendering of JavaScript when required, then applies your schema to produce clean, typed JSON.

Quick start with AlterLab Extract API

The following examples show how to call the extract endpoint for a PriceGrabber product page.

Python example

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "product_name": {
      "type": "string",
      "description": "The product name field"
    },
    "price": {
      "type": "string",
      "description": "The price field"
    },
    "store": {
      "type": "string",
      "description": "The store field"
    },
    "availability": {
      "type": "string",
      "description": "The availability field"
    },
    "url": {
      "type": "string",
      "description": "The url field"
    }
  }
}

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

cURL example

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

Batch and async usage

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

urls = [
  "https://pricegrabber.com/item1",
  "https://pricegrabber.com/item2",
  "https://pricegrabber.com/item3"
]

async def extract_one(url):
    schema = {
      "properties": {
        "product_name": {"type": "string"},
        "price": {"type": "string"},
        "store": {"type": "string"}
      }
    }
    resp = await client.extract_async(url=url, schema=schema)
    return resp.data

tasks = [extract_one(u) for u in urls]
results = await asyncio.gather(*tasks)
print(results)

Define your schema

The schema parameter is a standard JSON schema draft. It tells AlterLab which fields to extract and how to type them. The service then validates each response against this schema before returning it. This eliminates the need for manual string parsing or regex work.

{
  "type": "object",
  "properties": {
    "product_name": {"type": "string"},
    "price": {"type": "string"},
    "store": {"type": "string"},
    "availability": {"type": "string"},
    "url": {"type": "string"}
  }
}

When you include the schema in the request, AlterLab returns only the defined keys, each with the correct type. If a field cannot be determined, the result may be null, allowing your downstream code to handle missing data gracefully.

Handle pagination and scale

High‑volume pipelines often need to process hundreds of URLs. AlterLab supports batch requests and asynchronous jobs to keep throughput high while staying within rate limits.

• Use the /v1/batch endpoint to submit multiple URLs in a single POST.
• For very large jobs, create an async workflow that polls for completion and retrieves results once ready.
• Respect the per‑second request cap; AlterLab enforces fair usage to protect downstream services.

Cost scales with the complexity of the target page and the amount of data returned. Each extraction request includes an estimated cost preview, which you can query before committing. Costs are clamped between $0.001 and $0.50, and pricing details are available on the AlterLab pricing page.

Key takeaways

• Public price‑comparison data from PriceGrabber can be retrieved reliably via a schema‑driven Extract API.
• AlterLab handles proxy rotation, captcha solving, and rendering so you can focus on data modeling.
• Define a JSON schema, send the target URL, and receive validated, typed JSON without any parsing code.
• Preview extraction cost before execution and scale with batch or async workflows while staying within your budget.

For more details, see the full documentation at /docs/extract and the quick‑start guide at /docs/quickstart/installation.