We extract studio profiles, class schedules, instructor rosters, dynamic credit pricing, and amenity data from Classpass. 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 Studio Profiles objects from classpass.com. All fields typed and schema-versioned.
"studio_id": "ST-98214", "name": "Barry's Bootcamp", "address": "135 W 20th St, New York, NY 10011", "neighbourhood": "Chelsea", "rating": 4.9, "review_count": 14205, "amenities": "['Showers', 'Lockers', 'Towels']", "tags": "['HIIT', 'Strength', 'Treadmill']"
| # | studio_id | name | address | neighbourhood | city | coordinates |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Class Types objects from classpass.com. All fields typed and schema-versioned.
"class_id": "CL-44512", "studio_id": "ST-98214", "name": "Full Body (Lower Focus)", "duration_minutes": 50, "category": "HIIT", "difficulty_level": "Advanced", "equipment_required": "['Dumbbells', 'Treadmill']", "is_livestream": false
| # | class_id | studio_id | name | description | duration_minutes | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Schedules & Pricing objects from classpass.com. All fields typed and schema-versioned.
"schedule_id": "SCH-998123", "class_id": "CL-44512", "instructor_name": "Sarah J.", "start_time": "17:30:00", "date": "2026-08-14", "spots_available": 3, "credit_cost": 12, "dynamic_pricing_flag": true, "cancellation_window_hours": 12
| # | schedule_id | class_id | studio_id | instructor_name | start_time | end_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructors objects from classpass.com. All fields typed and schema-versioned.
"instructor_id": "INS-7761", "studio_id": "ST-98214", "name": "Sarah J.", "instagram_handle": "@sarahj_fit", "classes_taught": "['Full Body', 'Abs & Ass']", "average_rating": 4.95, "image_url": "https://images.classpass.com/instructor/7761.jpg"
| # | instructor_id | studio_id | name | bio | image_url | instagram_handle |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from classpass.com. All fields typed and schema-versioned.
"review_id": "REV-554129", "studio_id": "ST-98214", "user_first_name": "Michael", "date": "2026-08-10", "rating": 5, "comment": "Incredible energy. The treadmill section was brutal in the best way.", "class_attended": "Full Body (Lower Focus)", "instructor_name": "Sarah J."
| # | review_id | studio_id | user_first_name | date | rating | comment |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Classpass scraper extracts the complete fitness inventory graph: studios, real-time schedules, dynamic credit pricing, and instructor rosters, bypassing anti-bot measures to deliver clean datasets.
Extract studio names, addresses, coordinates, aggregated ratings, review counts, and amenity lists across global markets.
Capture daily and weekly schedules, including start times, end times, class duration, and real-time spot availability.
Track exact credit costs per class and detect dynamic surge pricing on high-demand inventory slots.
Extract instructor bios, profile images, social handles, and average ratings tied to specific studios and classes.
Scrape user reviews, star ratings, and textual feedback linked to specific classes and instructors.
Collect data across New York, London, Sydney, Singapore, and all other major Classpass operating cities.
Monitor credit cost fluctuations over time to understand inventory demand and platform pricing strategies.
Capture facility details like showers, parking, and strict cancellation window policies per studio.
Run one-off bulk exports or configure continuous pipelines at hourly cadences to catch schedule updates.
Brief in. Clean data out.
Provide target cities, studio IDs, or fitness categories. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and GraphQL query interception for classpass.com.
Schema validation, null-rate checks, and credit-price outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Classpass uses strict anti-bot systems and complex internal APIs. Here is how we maintain reliable extraction.
Classpass employs advanced bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full session management to avoid blocks.
Instead of brittle DOM parsing, we intercept internal GraphQL queries used by the Classpass frontend. This ensures highly structured, stable data retrieval for schedules and pricing.
Classpass inventory depends heavily on user location context. We manage geolocation headers and cookies dynamically to simulate searches across hundreds of distinct neighbourhoods.
For large market tracking, we maintain a hash index of last-seen schedule states. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, API schema drift, and coverage drops, responding before data quality degrades.
Boutique fitness studios monitor local rivals to compare class schedules, pricing tiers, and instructor rosters.
Fitness aggregators and franchises analyse studio density and review volume to find underserved neighbourhoods.
Pricing analysts track credit cost fluctuations to understand consumer demand and optimise their own pricing strategies.
Studio operators identify top-rated instructors in their city by analysing user reviews and class popularity.
Researchers track the growth of specific modalities, comparing the rise of pilates against traditional HIIT classes.
Machine learning teams use structured fitness datasets to train personalised workout recommendation engines.
"Classpass holds the most comprehensive, standardised inventory of boutique fitness schedules and dynamic pricing signals globally."
Extracting fitness inventory at scale requires intercepting complex GraphQL responses, bypassing strict anti-bot measures, and managing state across thousands of local searches. DataFlirt handles the infrastructure overhead so your team can focus on analysing market density and pricing strategies.
Everything supported by our classpass.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.
We maintain pools of residential ISP proxies across target regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. 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 classpass.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Classpass is generally permissible under applicable law. DataFlirt targets only public, non-authenticated studio, schedule, and pricing data. We do not extract personal user data or circumvent authentication walls. Clients should review platform terms 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 block rate spikes in real time and trigger pool rotation automatically.
We can extract data from any city where Classpass operates globally, including North America, Europe, Asia, and Australia, by simulating local geographic searches.
Real-time streaming pipelines achieve sub-60-minute latency for schedule and availability signals on a defined studio set. Full city refreshes at daily cadence complete within a 4-8 hour window.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per class slot for credit cost and availability from the date your pipeline starts.
We provide a sample run of up to 100 studios and their schedules as part of the pre-engagement scoping process, so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off studio directory dump or a continuous schedule monitoring feed across major cities, we scope, build, and operate the pipeline. Tell us what you need.
