We extract designer listings, historical sold prices, seller feedback, item measurements, and authentication badges from Grailed. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Active Listings objects from grailed.com. All fields typed and schema-versioned.
"listing_id": "39481029", "title": "Rick Owens Geobasket Leather Sneakers", "designer": "Rick Owens", "size": "US 10 / EU 43", "condition": "Gently Used", "price": 650.0, "authentication_badge": "Digitally Authenticated", "likes_count": 142
| # | listing_id | title | designer | size | condition | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sold Items objects from grailed.com. All fields typed and schema-versioned.
"listing_id": "28371920", "title": "Raf Simons Riot Riot Riot Camo Bomber", "designer": "Raf Simons", "sold_price": 45000.0, "listed_price": 50000.0, "sold_date": "2023-11-14T14:22:00Z", "seller_username": "archive_god", "authentication_badge": "Digitally Authenticated"
| # | listing_id | title | designer | size | condition | sold_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from grailed.com. All fields typed and schema-versioned.
"username": "archive_god", "feedback_score": 4.9, "feedback_count": 842, "items_sold": 1204, "items_for_sale": 156, "badge_trusted_seller": true, "badge_fast_shipper": true
| # | username | feedback_score | feedback_count | items_sold | items_for_sale | joined_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Designers objects from grailed.com. All fields typed and schema-versioned.
"designer_id": "rick-owens", "name": "Rick Owens", "follower_count": 482910, "listing_count": 15420, "category": "Avant Garde", "related_designers": "['Julius', 'Boris Bidjan Saberi', 'Ann Demeulemeester']", "average_price": 485.5
| # | designer_id | name | follower_count | listing_count | category | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Grailed scraper handles every layer of the marketplace: dynamic infinite-scroll feeds, GraphQL API interception, and anti-bot circumvention — delivering structured fashion data on your schedule.
Title, designer, size, condition, measurements, tags, and authentication status — scraped at the individual listing level.
Capture final transaction values and dates to build accurate pricing models for vintage and archival pieces.
Track seller feedback, transaction volume, location, and platform badges — Trusted Seller, Fast Shipper, Quick Responder.
Monitor listing price reductions, active negotiation margins, and time-on-market metrics across specific designers.
Extract and normalise pit-to-pit, length, shoulder, and sleeve metrics into structured JSON fields.
Log Grailed's 'Digitally Authenticated' and 'In-Hand' verification badges to filter high-confidence listings.
Brief in. Clean data out.
Provide designer URLs, category filters, keyword sets, or seller usernames. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for grailed.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Grailed employs aggressive anti-scraping measures to protect its marketplace data. Here is how we maintain stable extraction.
Grailed uses advanced bot protection that flags headless browsers and datacentre IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to bypass these layers.
Grailed is a heavy React application relying on infinite scroll and dynamic DOM updates. We run full Playwright browser sessions to trigger lazy-loaded elements and capture data that static HTTP clients cannot see.
Instead of relying solely on brittle DOM parsing, our pipeline intercepts Grailed's internal GraphQL API responses during the browser session — extracting clean, structured JSON payloads directly from the network tab.
For tracking active listings, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — such as price drops or sold status updates — reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, GraphQL schema drift, and coverage drops — responding before you notice missing data.
Train pricing models for vintage and archival fashion based on actual sold data rather than active listing aspirations.
Analyse listing velocity, like counts, and sell-through rates across specific designers to predict broader fashion trends.
Monitor top seller inventory, pricing strategies, and response times to optimise your own resale operations.
Collect image datasets of verified authentic items to train machine learning computer vision models for counterfeit detection.
Spot underpriced listings relative to historical sold averages in real-time for immediate acquisition.
Monitor unauthorised resale, grey market distribution, and MAP violations of current season inventory on the secondary market.
"Grailed holds the definitive pricing history for archival fashion and streetwear — but extracting that historical ledger requires bypassing aggressive anti-bot layers."
Most teams underestimate the investment required: reliable Grailed scraping requires residential proxies, full JavaScript rendering for React apps, GraphQL interception, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our grailed.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/UK/EU regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About grailed.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Grailed is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated listing, pricing, and seller data. We do not extract personal private messages or circumvent authentication walls. Clients should review Grailed's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass Datadome and Cloudflare. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. We can extract the final sold price, original listed price, and transaction date for items in the 'Sold' feed, providing accurate historical pricing data for archival pieces.
Real-time streaming pipelines achieve sub-15-minute latency for price updates on a defined set of monitored listings, delivered via Webhook for immediate processing.
Yes. We extract the raw measurement text and normalise it into structured JSON fields (e.g., pit-to-pit, length, shoulders) wherever sellers provide them in standard formats.
Our smallest packages start at a defined designer list or category set with daily delivery. For full-platform historical scrapes or real-time arbitrage feeds, we price based on compute volume and delivery frequency. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical sold price dump or a continuous price-monitoring feed across top designers — we scope, build, and operate the pipeline. Tell us what you need.
