We extract product listings, dual-tier pricing, inventory depth, outfit mappings, and customer reviews from Fabletics. 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 fabletics.com. All fields typed and schema-versioned.
"sku": "PT2145892", "title": "Oasis High-Waisted Legging", "category": "Bottoms", "sub_category": "Leggings", "guest_price": 74.95, "vip_price": 59.95, "fabric_type": "Motion365", "compression_level": "High"
| # | sku | title | category | sub_category | guest_price | vip_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from fabletics.com. All fields typed and schema-versioned.
"sku": "PT2145892", "guest_price": 74.95, "vip_price": 59.95, "promotional_price": 24.0, "discount_pct": 20, "new_vip_offer": true, "currency": "USD"
| # | sku | guest_price | vip_price | promotional_price | discount_pct | new_vip_offer |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Stock objects from fabletics.com. All fields typed and schema-versioned.
"sku": "PT2145892-BLK-M-R", "parent_id": "PT2145892", "colour_name": "Black", "size": "Medium", "inseam": "Regular", "in_stock": true, "low_stock_warning": false
| # | sku | parent_id | colour_name | size | inseam | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Outfits & Bundles objects from fabletics.com. All fields typed and schema-versioned.
"bundle_id": "BDL9921", "bundle_name": "The Core Essential Set", "component_skus": "['PT2145892', 'TP8821034']", "total_guest_price": 124.9, "total_vip_price": 89.95, "savings_pct": 28
| # | bundle_id | bundle_name | component_skus | total_guest_price | total_vip_price | savings_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from fabletics.com. All fields typed and schema-versioned.
"review_id": "REV883210", "sku": "PT2145892", "rating": 4.8, "fit_rating": "True to Size", "quality_rating": 5, "body_text": "Excellent compression and squat-proof.", "date_posted": "2023-11-14"
| # | review_id | sku | rating | reviewer_name | fit_rating | quality_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Fabletics scraper handles the complex React frontend, dual-tier pricing logic, and variant explosions across sizes, colours, and inseams.
Title, fabric details, compression levels, and care instructions scraped at the SKU level with parent-child variant mapping.
Capture both Guest and VIP pricing, alongside introductory offers and membership credit eligibility.
Extract component SKUs from styled lookbooks and bundles, calculating combined pricing and VIP savings.
Track stock depth across complex permutations of size, colour, and inseam lengths.
Extract customer feedback, star ratings, and specific fit indices like true-to-size metrics.
Scrape region-specific availability and pricing across UK, US, and EU storefronts.
Monitor flash sales, 2-for-24 introductory offers, and seasonal discounts.
Run continuous pipelines that only push diffs when prices or stock statuses change.
Extract structured JSON directly from Next.js hydration states for faster, more reliable data capture.
Brief in. Clean data out.
Provide category URLs, specific SKUs, or region requirements. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management to handle Fabletics anti-bot measures.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fabletics relies on a heavy JavaScript frontend and geo-fenced pricing. Here is how we ensure reliable data extraction.
Retailers deploy strict rate limiting. Our crawlers use residential ISP proxies with realistic browser fingerprints to maintain access without IP bans.
Fabletics uses a modern React frontend. We parse the underlying Next.js hydration state directly, bypassing fragile DOM selectors for structured product data.
Activewear has immense variant complexity. We recursively map every colour, size, and inseam combination to ensure complete catalogue coverage.
Pricing changes based on IP location. We route requests through region-specific proxies to capture accurate local currency pricing.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Activewear brands monitor VIP vs Guest pricing spreads to optimise their own subscription or loyalty models.
Retail strategists track colour and size availability over time to identify best-selling variants and stockout patterns.
Merchandising teams analyse new arrivals and fabric technology descriptions to spot emerging activewear trends.
Marketing teams track the frequency and depth of introductory offers like the 2-for-24 legging promotions.
Product developers analyse fit ratings and text reviews to improve their own garment sizing and fabric choices.
Machine learning teams use outfit bundle mappings to train visual recommendation engines.
"Fabletics hides its true inventory depth and pricing mechanics behind a JavaScript-heavy frontend and a dual-tier VIP membership model."
Extracting activewear data at scale requires more than simple HTTP requests. You must parse complex React hydration states, map multi-component outfit bundles, and rotate geo-specific residential proxies to capture accurate VIP pricing and regional stock availability. DataFlirt manages this infrastructure entirely.
Everything supported by our fabletics.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 deduplication. Playwright handles JavaScript rendering and SPA state extraction.
We maintain pools of residential ISP proxies across target regions to ensure accurate geo-pricing.
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 fabletics.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Fabletics is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
Yes. Fabletics renders promotional VIP pricing and Guest pricing in the public DOM and React hydration state to encourage sign-ups. We extract this public data without requiring account credentials.
Our schema recursively maps the parent product to every child SKU, capturing the exact stock status for each specific combination of colour, size, and inseam length.
Yes. We route requests through residential proxies located in the US, UK, DE, or other target regions to capture localised pricing and inventory.
Yes. We map the component SKUs that make up styled outfits and bundles, calculating the combined price and advertised savings percentage.
We configure pipelines to run at your required cadence, from daily full-catalogue refreshes to hourly checks on specific high-velocity categories.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price-monitoring across all variants - we scope, build, and operate the pipeline. Tell us what you need.
