We extract release calendars, style codes, retail prices, and historical sneaker catalogues from Kicksonfire. 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 Release Calendar objects from kicksonfire.com. All fields typed and schema-versioned.
"sneaker_name": "Air Jordan 1 Retro High OG", "brand": "Jordan", "model": "Air Jordan 1", "colourway": "Chicago Reimagined", "release_date": "2026-02-14T08:00:00Z", "retail_price": 180.0, "currency": "USD", "style_code": "DZ5485-612", "status": "upcoming"
| # | sneaker_name | brand | model | colourway | release_date | retail_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sneaker Database objects from kicksonfire.com. All fields typed and schema-versioned.
"id": "kof_89231", "name": "Nike Air Max 95 Neon", "brand": "Nike", "silhouette": "Air Max 95", "designer": "Sergio Lozano", "technology": "Air Max", "nickname": "Neon", "release_year": 1995, "original_price": 140.0
| # | id | name | brand | silhouette | designer | technology |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Market objects from kicksonfire.com. All fields typed and schema-versioned.
"style_code": "DZ5485-612", "retail_price": 180.0, "currency": "USD", "resale_estimate_low": 350.0, "resale_estimate_high": 420.0, "market_volatility": "high", "price_timestamp": "2026-05-12T09:14:00Z", "stock_status": "sold_out"
| # | style_code | retail_price | currency | resale_estimate_low | resale_estimate_high | market_volatility |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand Catalogues objects from kicksonfire.com. All fields typed and schema-versioned.
"brand_name": "Nike", "sub_brand": "Nike Sportswear", "total_models": 1240, "active_releases": 45, "historical_releases": 8942, "top_collaborators": "['Off-White', 'Travis Scott', 'Sacai']", "category": "Lifestyle", "url": "https://www.kicksonfire.com/category/nike/"
| # | brand_name | sub_brand | total_models | active_releases | historical_releases | top_collaborators |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Image Assets objects from kicksonfire.com. All fields typed and schema-versioned.
"style_code": "DZ5485-612", "primary_image": "https://images.kicksonfire.com/primary_dz5485_612.jpg", "gallery_images": "['https://images.kicksonfire.com/gal1_dz5485_612.jpg', 'https://images.kicksonfire.com/gal2_dz5485_612.jpg']", "angle_side": "https://images.kicksonfire.com/side_dz5485_612.jpg", "on_foot_images": true, "resolution": "1080x1080", "cdn_url": "images.kicksonfire.com"
| # | style_code | primary_image | gallery_images | angle_top | angle_side | angle_sole |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kicksonfire scraper handles every layer of the platform: release calendars, historical databases, style codes, and high-res image assets - with JavaScript rendering and anti-bot circumvention built in.
Extract upcoming and past release dates, retail prices, and drop times across all major brands and collaborations.
Capture historical archives including original release years, designers, technologies, and nicknames for thousands of silhouettes.
Download primary images, gallery sets, and on-foot shots directly from the CDN, mapped to their specific style codes.
Extract manufacturer style codes and SKUs to join Kicksonfire data with your existing inventory or authentication databases.
Navigate and extract data by brand, sub-brand, and specific silhouette hierarchies to build a structured taxonomy.
Capture outbound links to stockists and retailers for upcoming drops to monitor supply channels.
Bypass Cloudflare and Datadome protections using residential IP rotation and realistic browser finger-printing.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Convert local release times and countdown timers into standardised UTC timestamps for global tracking.
Brief in. Clean data out.
Provide target brands, release windows, or specific silhouettes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for kicksonfire.com.
Schema validation, null-rate checks, date format standardisation, and sample images before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sneaker platforms invest heavily in bot detection to protect their release data. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Sneaker sites deploy aggressive bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management - trained on real user behaviour patterns.
Kicksonfire release calendars and countdown timers rely heavily on client-side JavaScript. We run full Playwright browser sessions to execute scripts and trigger lazy-loaded content, capturing data that headless HTTP clients miss entirely.
Scraping thousands of high-res images quickly triggers CDN rate limits or IP bans. We manage connection pooling and throttle concurrent downloads to extract visual assets safely without degrading pipeline reliability.
Release dates are often displayed in local timezones or relative countdowns. Our pipeline parses and normalises all temporal data into strict UTC timestamps, ensuring your database remains consistent across global drops.
Media sites change their DOM structure frequently. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and text-pattern matching - so a layout change does not break your data pipeline overnight.
Secondary market platforms index upcoming releases and retail prices to establish baseline valuations before drops.
Authentication services build reference libraries of style codes, colourways, and original retail prices to verify physical inventory.
Machine learning teams scrape high-res image galleries mapped to specific SKUs to train sneaker recognition and authentication models.
Boutiques and retailers track release calendars to align their own marketing schedules and monitor stockist allocations.
Consumer applications populate their internal databases with historical archives and release dates without manual data entry.
Fashion analysts track the frequency of specific silhouettes, brand collaborations, and colourway trends over time.
"Kicksonfire holds one of the most comprehensive historical sneaker archives on the web - but turning editorial content into structured SKU data requires a dedicated pipeline."
Most teams underestimate the investment required: reliable sneaker scraping requires residential proxies, full JavaScript rendering for calendars, image CDN management, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our kicksonfire.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 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 kicksonfire.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Kicksonfire is generally permissible under applicable law. DataFlirt targets only public, non-authenticated release dates, pricing, and historical catalogue data. We do not extract personal data or circumvent authentication walls. Clients should review Kicksonfire'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. We monitor for CAPTCHA rate spikes in real time and trigger solver queues automatically to bypass Cloudflare and Datadome challenges.
Yes. We can extract direct CDN URLs for primary images, gallery shots, and on-foot angles. We can deliver these as URL arrays in the JSON/CSV output, or download the binary assets directly to your S3 bucket mapped by style code.
Release calendars can be refreshed on a daily or hourly cadence depending on your requirements. Changes to drop dates or retail prices are captured in subsequent runs using our change-detection diffing system.
Yes. We can perform a one-off historical backfill of the entire Kicksonfire database, capturing thousands of past releases, original retail prices, and silhouette details dating back years.
Our smallest packages start at a defined set of brands or a specific historical backfill volume. For continuous daily monitoring of the release calendar, we price based on 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 catalogue dump or a continuous release calendar feed - we scope, build, and operate the pipeline. Tell us what you need.
