We extract designer apparel listings, inventory depth, pricing signals, and sizing metrics from Revolve. 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 revolve.com. All fields typed and schema-versioned.
"id": "REVO-WD123", "brand": "NBD", "title": "Navarro Midi Dress", "price": 198.0, "currency": "USD", "category": "Dresses", "is_preorder": false, "model_size": "Height 5'9", Waist 24", Bust 32", Hips 34""
| # | id | url | brand | title | category | sub_category |
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Complete list of extractable fields for Inventory & Sizing objects from revolve.com. All fields typed and schema-versioned.
"product_id": "REVO-WD123", "size": "XS", "in_stock": true, "low_stock_warning": true, "waitlist_available": false, "sku": "NBD-WD456", "stock_timestamp": "2023-10-24T14:22:00Z"
| # | product_id | size | in_stock | low_stock_warning | waitlist_available | exact_measurements |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Markdowns objects from revolve.com. All fields typed and schema-versioned.
"product_id": "REVO-WD123", "current_price": 138.0, "retail_price": 198.0, "discount_pct": 30, "final_sale": false, "promo_eligible": true, "currency": "USD"
| # | product_id | current_price | retail_price | discount_pct | final_sale | promo_eligible |
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Our Revolve scraper extracts the full depth of the designer catalogue: complex size variants, dynamic stock states, markdown pricing, and material data — with JavaScript rendering and anti-bot circumvention built in.
Brand, title, category taxonomy, and detailed description text — mapped across Revolve's entire brand portfolio.
Capture granular model measurements, specific garment dimensions, and size-chart mapping for precise fit analysis.
Monitor stock availability per size variant, low-stock warnings, and waitlist status to gauge demand velocity.
Track original retail price, current markdown, final sale status, and promo code eligibility across all SKUs.
Extract fabric composition percentages, care instructions, and manufacturing origin for supply chain analysis.
Identify upcoming drops and restocks by monitoring pre-order availability and waitlist activation at the size level.
Capture all product angle URLs, zoom variants, and video asset links directly from Revolve's CDN.
Brief in. Clean data out.
Provide brand lists, category URLs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and rate-limit handling for revolve.com.
Schema validation, null-rate checks, price-outlier detection, and variant mapping before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fashion retail sites employ aggressive bot protection and complex state management for inventory. Here is how we maintain stable extraction.
Revolve handles sizes, colours, and stock states via complex frontend state. We execute full JavaScript rendering to map every size variant to its exact stock status and SKU, ensuring no missing inventory data.
Retailers use strict WAF rules to block datacenter IPs. We route all requests through US-based residential proxies with forged TLS fingerprints and realistic request headers to bypass perimeter defenses.
Revolve alters pricing and availability based on the user's IP and session cookies. Our pipeline forces specific geographic contexts—ensuring you collect USD pricing or localised currency variants reliably.
Fashion sites update their frontend frameworks frequently. We use multi-layered selector chains—combining CSS, XPath, and Next.js hydration state extraction—to prevent pipeline breakage during site updates.
Instead of exporting 80,000 unchanged products daily, we hash the payload and only emit records where price, stock, or waitlist status has changed—saving compute and downstream processing costs.
Fashion retailers track Revolve's markdown cadence, promo eligibility, and final sale triggers to optimise their own pricing strategies.
Analysts monitor size-level stockouts, waitlist activations, and low-stock warnings to predict trend velocity and brand performance.
Brands monitor their own wholesale presence on Revolve, tracking category placement, share of shelf, and out-of-stock rates.
Apparel manufacturers aggregate model measurements and garment dimensions to benchmark sizing standards across designer brands.
Product teams analyse fabric composition data, colour prevalence, and silhouette descriptions to inform upcoming collection designs.
Luxury brands audit Revolve's pricing against Minimum Advertised Price (MAP) policies and track authorised distribution channels.
"Revolve's catalogue is a real-time indicator of premium fashion demand—but extracting size-level inventory depth requires sophisticated rendering."
Most teams fail at fashion scraping because they ignore variant-level state. Revolve's stock data is heavily reliant on JavaScript hydration. DataFlirt handles the rendering, proxy rotation, and schema maintenance, delivering clean, analytics-ready data directly to your warehouse.
Everything supported by our revolve.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 and interaction flows for complex size variants.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to maintain regional pricing context.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About revolve.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Revolve is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls.
Revolve loads variant data dynamically. We use Playwright to execute JavaScript and hydrate the frontend state, allowing us to map every size to its specific SKU, stock status, and waitlist eligibility.
Yes. We use geolocation-targeted residential proxies and specific session cookies to force Revolve to display pricing in your target currency (e.g., USD, GBP, EUR, AUD).
For targeted brand or category monitoring, we can configure pipelines to run hourly. Full catalogue refreshes typically run at a daily cadence to balance completeness with compute efficiency.
Yes. Since FWRD is Revolve's luxury sister site and shares similar infrastructure, we can easily configure parallel pipelines to extract cross-listed inventory and FWRD-exclusive designer data.
Our smallest packages start at a defined brand or category list with weekly delivery. For full-catalogue daily extraction, we price based on volume and delivery frequency. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off designer catalogue dump or continuous inventory monitoring across 80,000 SKUs — we scope, build, and operate the pipeline. Tell us what you need.
