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Contemporary & direct-to-consumer

Quince Product Data

Prices, variants, availability, and images from quince.com — normalised, validated, and delivered into your stack. No collector to build, and none to maintain.

source specification

Source
quince.com
Segment
Contemporary & direct-to-consumer
Origin
United States — factory-direct essentials
Categories
Womenswear, Menswear, Home, Bags, Accessories
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily

about the source

What Quince sells, and how its catalogue behaves

Quince built its business on a single argument: cut the brand markup and sell cashmere, silk, and leather essentials at factory-adjacent prices. The catalogue spans apparel, home, and accessories, and almost every listing is framed as an explicit comparison against what a traditional brand would charge.

why teams track it

Why Quince data is worth having

Quince is the most aggressive price disruptor in premium basics, which makes it the single most useful competitive reference for anyone selling cashmere, silk, or leather essentials. When Quince moves a price on a category, it moves the perceived fair value of that category.

Visit quince.com

collection method

How we collect Quince

Quince is collected through the structured product data embedded in Quince product pages, including the schema markup the storefront publishes for its own SEO. Every source gets a dedicated collector rather than a generic crawler, because the field detail that makes this data useful only survives if the extraction is built for the site it runs against.

Structured at the source

Records are read from quince.com's own structured responses wherever they exist, rather than reconstructed from page markup. That keeps the feed stable across visual redesigns.

Validated every run

Each field is checked against expected types and historical ranges. A collector producing anomalies is quarantined and repaired upstream instead of emitting bad prices into your pipeline.

Normalised to one schema

Every brand in the catalogue lands on the same schema, so adding a source is a configuration change on your side rather than another integration to write.

15min
Fastest refresh
99.9%
Uptime SLA
3
Delivery formats
49+
Fashion sources

what you get back

Fields in the Quince feed

A normalised core that is identical across every source, plus the attributes that are specific to this one.

Standard across every source

  • Product name, brand, and source URL
  • Current price, original price, and currency
  • Category and subcategory as the source classifies them
  • Colour, size, and variant availability
  • Product images, deduplicated across variants
  • Description, composition, and care text
  • Collection timestamp on every record

Specific to Quince

  • Material composition, which is central to the Quince proposition
  • Structured schema data published by the storefront itself
  • Cross-category coverage spanning apparel, home, and accessories
  • Colourway variants with per-colour imagery

SOC 2 Type II

Audited controls across security, availability, and confidentiality. Report available under NDA.

GDPR & CCPA

Public catalogue data only. No personal data collected, and a DPA is available on request.

99.9% Uptime SLA

Contractual availability with monitored collectors and a public status page.

Data residency

Choose EU or US processing and storage regions to match your obligations.

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

Because it prices premium materials against factory cost rather than against category convention. If you sell cashmere or silk essentials, Quince is the number your customer is comparing you to, and its price on a comparable fibre grade is the most actionable single data point in that category.
Yes. Quince runs bedding, bath, and home goods on the same catalogue structure as apparel, so the collector covers them under the same schema with a category field. That is unusual — most apparel feeds stop at clothing.
Into the systems you already run, rather than through an API you have to integrate against. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks can push price and availability changes as they are detected. A solutions engineer fixes the schema, cadence, and destination with you during onboarding, so the first delivery already matches your pipeline.
Maintaining the collector is our job, not yours. Every field is validated against expected types and historical ranges on each run, and a collector that starts producing anomalies is quarantined rather than allowed to emit bad records. We repair it upstream, and the schema we deliver to you does not move — which is the entire reason to buy this rather than run a scraper in-house.
We collect only publicly visible catalogue data — the prices, descriptions, images, and availability any shopper sees without logging in. We do not bypass authentication, we do not collect personal data, and we honour rate limits so the source is never disrupted. Our infrastructure is SOC 2 Type II certified, our processing is GDPR and CCPA compliant, and we sign DPAs as part of procurement.
Pricing is scoped per engagement, driven by catalogue size, refresh cadence, the number of locales you need, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample pulled from the live quince.com catalogue, so you can check the data against your own benchmarks before committing to anything.

Still have questions?

Talk to an engineer

Ready to Get Started?

Talk to us about your sources and volume. We'll return a sample dataset from your target sites before you commit to anything.

SOC 2 Type II
GDPR & CCPA compliant
99.9% uptime SLA

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