We extract designer catalogues, rental pricing, size availability, and user fit reviews from Rent the Runway. 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 Product Listings objects from renttherunway.com. All fields typed and schema-versioned.
"product_id": "PROD94821", "designer_name": "Reformation", "title": "Juliette Dress", "retail_price": 248.0, "rental_price_4_day": 35.0, "rental_price_8_day": 55.0, "colour": "Emerald Green", "available_sizes": "['0', '2', '4', '6', '8', '10', '12']"
| # | product_id | designer_name | title | retail_price | rental_price_4_day | rental_price_8_day |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Fit Reviews objects from renttherunway.com. All fields typed and schema-versioned.
"review_id": "REV8839201", "product_id": "PROD94821", "overall_fit": "True to Size", "size_worn": "4", "user_height": "5'6"", "user_weight": "135 lbs", "user_bust": "34B", "rating": 5, "date_posted": "2023-10-14"
| # | review_id | product_id | user_nickname | overall_fit | size_worn | user_height |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Availability Calendars objects from renttherunway.com. All fields typed and schema-versioned.
"product_id": "PROD94821", "size": "4", "date_start": "2023-11-01", "date_end": "2023-11-04", "status": "unavailable", "rental_period": "4-day", "zip_code": "10001", "last_checked": "2023-10-25T08:14:00Z"
| # | product_id | size | date_start | date_end | status | rental_period |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Stylist & Occasion Data objects from renttherunway.com. All fields typed and schema-versioned.
"product_id": "PROD94821", "stylist_notes": "Fitted bodice with a relaxed skirt. Great for pear shapes.", "occasion_tags": "['Wedding Guest', 'Date Night', 'Party']", "body_type_recommendations": "['Pear', 'Hourglass']", "style_tags": "['Romantic', 'Classic']", "trend_tags": "['Floral', 'Midi']", "pairing_suggestions": "['Strappy heels', 'Statement earrings']"
| # | product_id | stylist_notes | occasion_tags | body_type_recommendations | age_group_recommendations | style_tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Designer Profiles objects from renttherunway.com. All fields typed and schema-versioned.
"designer_id": "DES442", "designer_name": "Reformation", "brand_description": "Sustainable fashion brand focusing on vintage-inspired designs.", "total_styles": 342, "active_styles": 128, "primary_categories": "['Dresses', 'Tops']", "price_tier": "Mid-Luxury", "designer_url": "https://www.renttherunway.com/designers/reformation"
| # | designer_id | designer_name | brand_description | total_styles | active_styles | primary_categories |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipelines handle RTR's dynamic inventory model: date-based availability, subscription tier pricing, and complex fit review matrices with user body measurements.
Full product metadata including title, designer, retail price, rental tiers, colour variants, and detailed descriptions.
Capture 4-day, 8-day, and clearance purchase prices alongside original retail values for margin analysis.
Extract standard, plus, petite, and maternity size availability across the entire catalogue.
Extract user height, weight, bust size, and typical size worn alongside text reviews and star ratings.
Track inventory dates and stock-outs by simulating date picker interactions for specific rental windows.
Extract stylist notes, wedding, gala, and vacation occasion tags to build recommendation engines.
Capture front, back, detail, and user-uploaded review images via CDN URLs.
Material composition percentages and cleaning requirements parsed into structured fields.
Only emit diffs for availability and pricing changes, reducing storage bloat for downstream pipelines.
Brief in. Clean data out.
Provide designer URLs, category filters, or specific product IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for renttherunway.com.
Schema validation, null-rate checks, and sample fit reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
RTR relies heavily on dynamic date pickers and location-based availability. We manage the session state required to extract accurate inventory.
Availability is not static HTML. We use full Playwright sessions to interact with date pickers, simulating user rental periods to extract true inventory status.
Shipping times affect availability. Our crawlers inject target zip codes to capture accurate location-specific inventory data.
Fit reviews are loaded via JavaScript pagination. We intercept XHR requests to extract the complete review corpus without missing records.
RTR employs strict bot detection. We use US-based residential proxies and spoofed browser fingerprints to maintain high success rates.
Layouts differ between standard rentals and clearance sales. We use multiple fallback chains to ensure consistent data extraction across all page types.
Compare rental pricing models against retail values to optimise alternative fashion subscription services.
Analyze availability calendars to identify high-demand designers and seasonal style trends.
Train machine learning models on user measurements versus fit outcomes to improve sizing recommendations.
Monitor designer popularity and review sentiment to inform wholesale purchasing decisions.
Retailers use rental velocity and occasion tags to plan seasonal inventory assortments.
Run NLP on fit reviews to understand common fit issues and fabric complaints for specific designers.
"Rent the Runway holds the industry's most comprehensive dataset on how designer garments actually fit real women across thousands of body types."
Extracting this data requires navigating complex date-based availability APIs, infinite-scroll review sections, and strict bot protection. DataFlirt manages the proxy rotation, JavaScript execution, and schema parsing so your data engineering team receives structured, analysis-ready tables.
Everything supported by our renttherunway.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 and retry logic. Playwright handles JavaScript rendering, date picker interactions, and dynamic content.
We maintain pools of US residential ISP proxies to bypass bot protection. Rotation happens per-request with sticky sessions for calendar checks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About renttherunway.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use Playwright to simulate user interactions with the date picker UI, capturing the resulting inventory status for specific rental windows and zip codes.
Yes. We extract user height, weight, bust size, and typical size worn, mapping these data points to the specific size rented and the overall fit rating.
We configure pipelines to run at your required cadence. Daily runs are standard for full catalogues, with hourly checks available for high-demand subsets.
Yes. We intercept the underlying XHR requests used for pagination, ensuring we capture the complete review history for every product.
Our smallest packages start at a defined designer list or category subset with weekly delivery. 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 catalogue dump or a continuous fit-review feed across 15,000 styles, we scope, build, and operate the pipeline. Tell us what you need.
