We extract closet listings, historical sold prices, brand catalogues, seller intelligence, and Posh Party trends from Poshmark. 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 poshmark.com. All fields typed and schema-versioned.
"listing_id": "64b8a9f2e4b0d1a2", "title": "Lululemon Align High-Rise Pant 25"", "brand": "Lululemon", "size": "4", "condition": "Excellent", "nwt_status": false, "original_price": 98.0, "listing_price": 65.0, "likes_count": 42, "seller_username": "activewear_finds"
| # | listing_id | title | description | brand | size | condition |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Sold Items objects from poshmark.com. All fields typed and schema-versioned.
"listing_id": "64b8a9f2e4b0d1b5", "title": "Patagonia Better Sweater Quarter-Zip", "brand": "Patagonia", "size": "M", "original_price": 139.0, "sold_price": 85.0, "sold_date": "2023-10-14T18:22:00Z", "seller_username": "outdoor_gear_co", "condition": "Good", "days_to_sell": 14
| # | listing_id | title | brand | size | original_price | sold_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Seller Closets objects from poshmark.com. All fields typed and schema-versioned.
"username": "vintage_vault", "display_name": "The Vintage Vault", "follower_count": 14205, "following_count": 850, "listings_count": 412, "shares_count": 89430, "joined_date": "2018-04-12", "posher_status": "Ambassador II", "boutique_certified": true
| # | username | display_name | follower_count | following_count | listings_count | shares_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Poshmark scraper navigates infinite scroll APIs, category taxonomies, and dynamic price drops to extract structured closet and transaction data.
Title, description, size, NWT status, brand, pricing, and high-resolution images — extracted across thousands of active listings.
Extract actual cleared prices from sold listings to build accurate resale valuation models and track category depreciation.
Track follower counts, share velocity, active listing volume, Posh Ambassador status, and Boutique certification.
Monitor specific designers, sub-categories, and seasonal trends across the entire platform with structured taxonomy.
Capture price reductions, shipping discount triggers, and offer-to-liker historical patterns timestamped per crawl.
Scrape themed party showrooms for curated trending items, host picks, and rapid-turnover inventory.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand lists, seller usernames, or category URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for poshmark.com.
Schema validation, null-rate checks, price-outlier detection, and sample closets before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Poshmark relies heavily on infinite scroll and dynamic API endpoints. Here is how we build resilient extraction pipelines.
Poshmark closets and search results use infinite scroll. We intercept internal GraphQL and REST API calls rather than rendering expensive DOM elements, reducing latency and ensuring complete data capture without browser memory leaks.
Poshmark rate-limits aggressive IP blocks. We distribute requests across US-based residential proxies, rotating per block threshold and mimicking organic session duration to maintain continuous extraction.
Sold listings are often buried or removed from primary search indices. We maintain historical lookup indices and target specific sold-item endpoints to capture actual clearing prices, not just initial listing prices.
We track price drops and status updates (Active to Sold) using hash-based diffing. Subsequent runs only push state changes, minimising storage bloat and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual.
Authentication and resale platforms use historical Poshmark sold data to price incoming second-hand inventory accurately.
Apparel brands monitor secondary market volume, resale value retention, and counterfeit prevalence across their product lines.
Retailers track how quickly specific categories sell and at what discount to original retail price to inform primary market markdowns.
Computer vision models use Poshmark listing images and descriptions to train clothing classification and defect detection algorithms.
Fashion analysts track search velocity, Posh Party themes, and 'Likes' accumulation to predict upcoming seasonal trends.
PE firms track active seller volume, listing velocity, and GMV indicators to evaluate marketplace health and category dominance.
"Poshmark contains the most accurate secondary market pricing data for apparel on the internet — but extracting historical sold prices requires navigating complex infinite-scroll APIs."
Most teams underestimate the investment required: reliable Poshmark scraping requires US residential proxies, API interception, infinite scroll management, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our poshmark.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. 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 poshmark.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Poshmark 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 data, circumvent authentication walls, or violate GDPR.
Yes. We target specific sold-item endpoints to capture the actual cleared price and sale date, providing an accurate view of secondary market valuation rather than just the initial asking price.
Instead of rendering the DOM and physically scrolling, we intercept the underlying GraphQL and REST API requests that populate the feed. This ensures fast, comprehensive extraction without pagination limits or memory bloat.
Yes. Pipelines can be scoped to specific brand catalogues, category URLs, or lists of seller usernames. We design the target scope during onboarding.
Real-time streaming pipelines achieve sub-60-minute latency for status changes (Active to Sold). Full category refreshes at daily cadence complete within a 6-12 hour window depending on catalogue size.
No. We strictly extract publicly visible data. Private offer negotiations, buyer shipping addresses, and seller earnings dashboards are gated and inaccessible.
Our smallest packages start at a defined target list (typically 10,000-50,000 listings) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off brand catalogue dump or a continuous price-monitoring feed across 500K listings — we scope, build, and operate the pipeline. Tell us what you need.
