Sephora 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 Sephora data via API, use a schema-based extraction engine like AlterLab. By sending a POST request with a target URL and a JSON schema, you can bypass HTML parsing and receive typed JSON containing product titles, prices, and SKUs directly.

Why use Sephora data?

E-commerce data from high-volume retailers like Sephora is critical for several engineering and business applications:

– AI Training & RAG: Feeding accurate product catalogs into Large Language Models (LLMs) to power beauty-focused AI shopping assistants. – Competitive Intelligence: Monitoring price fluctuations and promotional cycles across beauty categories in real-time. – Market Analytics: Analyzing product availability and rating trends to identify gaps in the beauty market.

What data can you extract?

When building a data pipeline for e-commerce, the goal is to move from unstructured HTML to a typed database. The following publicly available fields are typically extracted:

– Product Title: The full name of the item for indexing. – Price & Currency: Numerical values for price tracking and currency codes (e.g., USD) for global analysis. – SKU/Product ID: The unique identifier used to map products across different sessions. – Availability: Status indicators (e.g., "In Stock", "Out of Stock") to track inventory levels. – Ratings & Reviews: Average star ratings and total review counts to gauge product sentiment.

The extraction approach

Traditional web scraping relies on CSS selectors or XPath. This approach is fragile; if Sephora updates a single class name in their frontend framework, your entire pipeline breaks.

A data API approach is different. Instead of targeting the location of the data, you define the shape of the data you want. AlterLab uses LLM-powered extraction (Cortex AI) to identify the relevant information regardless of the underlying HTML structure. This shifts the maintenance burden from the developer to the API provider.

If you are new to the platform, refer to our Getting started guide to configure your environment.

Quick start with AlterLab Extract API

The Extract API allows you to define a JSON schema and receive a validated response. You can find the full technical specifications in the Extract API docs.

Python Implementation

The following example demonstrates how to extract core product details using the official Python SDK.

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://sephora.com/example-page",
    schema=schema,
)
print(result.data)

cURL Implementation

For lightweight integrations or shell scripts, use the REST endpoint.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://sephora.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 standards to ensure the output is typed and predictable. When the API processes a page, it validates the extracted data against your properties before returning the response.

Example Structured Output:

{
  "title": "High-Performance Glow Serum",
  "price": "48.00",
  "currency": "USD",
  "sku": "SEPH-12345-B",
  "availability": "In Stock",
  "rating": "4.7/5"
}

By specifying type: "string" or type: "number", you eliminate the need for post-processing regex or manual type casting in your application logic.

Handle pagination and scale

When extracting thousands of products, synchronous requests are inefficient. For high-volume pipelines, use asynchronous jobs.

Asynchronous Batch Extraction

Instead of waiting for a response, you can submit a batch of URLs and poll for the results or use a webhook.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

urls = [
    "https://sephora.com/p1",
    "https://sephora.com/p2",
    "https://sephora.com/p3"
]

# Submit async job
job = client.extract_batch(
    urls=urls,
    schema=schema,
    webhook_url="https://your-server.com/webhook"
)

print(f"Job submitted: {job.id}")

Cost and Rate Management

Scale requires cost predictability. AlterLab provides an estimation endpoint to preview the cost of an extraction before committing to the call.

Costs are clamped between $0.001 and $0.50 per request. If you use a BYOK (Bring Your Own Key) setup, the orchestration fee is reduced to 300 µ¢. For detailed billing and limit configurations, visit AlterLab pricing.

Key takeaways

– Move beyond selectors: Use schema-based extraction to avoid pipeline breakage. – Prioritize typed data: Define your JSON schema upfront to receive clean, validated output. – Scale asynchronously: Use batch jobs and webhooks for large-scale e-commerce datasets. – Stay compliant: Always respect robots.txt and extract only publicly available data.