We extract gym locations, class timetables, amenity lists, and wellness partner catalogues from Gympass. 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 Gyms & Studios objects from gympass.com. All fields typed and schema-versioned.
"gym_id": "GYM-89421", "name": "Equinox Hudson Yards", "category": "Premium Fitness", "required_tier": "Diamond", "city": "New York", "rating": 4.8, "review_count": 1240, "latitude": 40.7538, "longitude": -74.0011
| # | gym_id | name | category | required_tier | address_line_1 | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Class Schedules objects from gympass.com. All fields typed and schema-versioned.
"class_id": "CLS-55912", "gym_id": "GYM-89421", "class_name": "Vinyasa Flow Yoga", "instructor_name": "Sarah Jenkins", "start_time": "2026-10-14T18:00:00Z", "duration_minutes": 60, "category": "Yoga", "spots_available": 12, "booking_required": true
| # | class_id | gym_id | class_name | instructor_name | start_time | end_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities objects from gympass.com. All fields typed and schema-versioned.
"gym_id": "GYM-89421", "has_pool": true, "has_parking": false, "has_showers": true, "has_sauna": true, "has_towel_service": true, "has_wifi": true, "last_updated": "2026-10-12T09:00:00Z"
| # | gym_id | has_pool | has_parking | has_showers | has_lockers | has_wifi |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Wellness Apps objects from gympass.com. All fields typed and schema-versioned.
"app_id": "APP-104", "app_name": "Calm", "category": "Mental Health", "required_tier": "Gold", "supported_platforms": "['iOS', 'Android', 'Web']", "rating": 4.9, "review_count": 45021, "integration_type": "SSO"
| # | app_id | app_name | developer | category | required_tier | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from gympass.com. All fields typed and schema-versioned.
"review_id": "REV-99210", "gym_id": "GYM-89421", "rating": 5, "review_text": "Incredible facilities and top tier equipment. The rooftop pool is unmatched.", "post_date": "2026-09-28", "helpful_votes": 34, "facility_cleanliness_rating": 5, "staff_friendliness_rating": 4
| # | review_id | gym_id | user_id | rating | review_text | visit_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Gympass scraper handles geographic search variations, dynamic class schedules, and tier-gated visibility rules with JavaScript rendering and session management built in.
Extract precise addresses, latitude and longitude coordinates, contact details, and operating hours for every partner facility globally.
Capture daily schedules, instructor names, class durations, and category tags across thousands of studios simultaneously.
Identify exactly which subscription tier is required to access specific gyms, studios, or premium wellness applications.
Categorise facilities by available amenities including pools, parking, showers, towel service, and specialised equipment.
Extract the complete database of digital wellness partners, including mental health, nutrition, and personal training apps.
Gather data on fitness professionals, including their biographies, specialties, and the specific classes they teach.
Extract user ratings, detailed text reviews, and specific feedback on facility cleanliness and equipment quality.
Perform radius-based scraping using coordinate grids to ensure complete coverage of urban and suburban fitness markets.
Run continuous pipelines to capture class schedule changes, new gym additions, and tier requirement adjustments in real time.
Brief in. Clean data out.
Provide target cities, coordinates, or specific gym categories. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, geographic proxy rotation, and session management for gympass.com.
Schema validation, null-rate checks, and coordinate verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Gympass relies on map-based dynamic loading and complex session states. Here is how we maintain reliable extraction pipelines.
Gympass surfaces locations through a map interface that requires precise API coordinate bounding boxes. We simulate these geographic queries programmatically to ensure no facility is missed during extraction.
Class timetables and dynamic availability load via complex JavaScript frameworks. We run full Playwright browser sessions to hydrate these components properly before extraction.
Gympass alters visibility based on the user location. We route requests through residential proxies physically located in the target search radius to capture accurate regional data.
For massive class schedules, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or coverage drops and respond before you notice.
Fitness aggregators and boutique studio chains track Gympass partner growth and tier placement to optimise their own pricing strategies.
HR departments and wellness consultants analyse tier requirements to evaluate the true value of corporate fitness subsidies.
Commercial property developers use gym density and amenity data to identify underserved neighbourhoods for new fitness facility investments.
Analysts track the rise of specific class categories and wellness apps to forecast broader health and fitness industry trends.
Health insurance providers integrate Gympass facility data to enrich their own member wellness portals and directories.
Independent gyms monitor which competitors join the Gympass network and at which tier to position their own membership fees.
"Gympass maps the global fitness ecosystem, but extracting structured class and tier data requires navigating complex geographic search parameters."
Most teams underestimate the investment required: reliable Gympass scraping requires geographic IP matching, full JavaScript rendering for map interfaces, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our gympass.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, map interactions, and complex session states.
We maintain pools of residential ISP proxies mapped to specific global regions to ensure accurate local search results and bypass geo-blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About gympass.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Gympass is generally permissible under applicable law. DataFlirt targets only public, non-authenticated facility, class, and tier data. We do not extract personal user data or circumvent authentication walls. Clients should review the Gympass terms of service and consult legal counsel for specific use cases.
We use coordinate bounding boxes and route requests through residential ISP proxies located in the target region. This ensures we capture the exact gyms and classes visible to a user in that specific geographic area.
Yes. We can extract daily and weekly timetables, including instructor names, class categories, and durations, for any facility listed on the platform.
Real-time streaming pipelines achieve sub-60-minute latency for schedule updates on a defined facility set. Full city-wide refreshes at daily cadence complete within a 4-8 hour window depending on geographic size.
Yes. Every facility record includes the specific Gympass subscription tier required for entry, allowing you to map the value of different plan levels.
Our smallest packages start at a defined city or region list with weekly delivery. For global coverage or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 facilities or 5 target cities as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off gym catalogue dump or a continuous class schedule feed across 50 cities, we scope, build, and operate the pipeline. Tell us what you need.
