We extract route networks, base fares, tax breakdowns, and ancillary pricing from Volaris. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Flight Schedules objects from volaris.com. All fields typed and schema-versioned.
"flight_number": "Y4 821", "origin_iata": "MEX", "destination_iata": "CUN", "departure_time_local": "2026-10-14T08:30:00", "arrival_time_local": "2026-10-14T11:45:00", "duration_minutes": 135, "aircraft_type": "Airbus A320neo", "stops": 0
| # | flight_number | origin_iata | destination_iata | departure_time_local | arrival_time_local | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fares & Taxes objects from volaris.com. All fields typed and schema-versioned.
"flight_number": "Y4 821", "departure_date": "2026-10-14", "fare_class": "Zero", "base_fare": 450.0, "tua_tax": 680.0, "other_taxes": 120.0, "total_price": 1250.0, "currency": "MXN", "v_club_discount": 150.0
| # | flight_number | departure_date | fare_class | base_fare | tua_tax | other_taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from volaris.com. All fields typed and schema-versioned.
"flight_number": "Y4 821", "route": "MEX-CUN", "carry_on_fee": 350.0, "checked_bag_fee_1": 550.0, "seat_selection_min": 99.0, "seat_selection_max": 399.0, "priority_boarding_fee": 150.0, "pet_in_cabin_fee": 950.0
| # | flight_number | route | carry_on_fee | checked_bag_fee_1 | checked_bag_fee_2 | seat_selection_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from volaris.com. All fields typed and schema-versioned.
"origin_code": "MEX", "origin_city": "Mexico City", "destination_code": "LAX", "destination_city": "Los Angeles", "is_direct": true, "frequency_weekly": 14, "seasonal_route": false, "distance_km": 2495
| # | origin_code | origin_city | origin_country | destination_code | destination_city | destination_country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from volaris.com. All fields typed and schema-versioned.
"flight_number": "Y4 821", "departure_date": "2026-10-14", "total_capacity": 186, "seats_available": 42, "premium_seats_available": 4, "exit_row_available": true, "seat_map_timestamp": "2026-09-01T14:22:10Z"
| # | flight_number | departure_date | total_capacity | seats_booked | seats_available | premium_seats_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Volaris scraper navigates multi-step booking flows, handles dynamic session tokens, and bypasses aviation bot protection to extract accurate pricing and availability data.
Extract departure times, arrival times, flight numbers, aircraft types, and duration across the entire Volaris network.
Capture base fares across all Volaris tiers including Zero, Basic, and Plus. Timestamped pricing to monitor yield management changes.
Isolate the base fare from the Tarifa de Uso de Aeropuerto (TUA) and government taxes to understand true pricing structures.
Extract costs for carry on luggage, checked bags, seat selection, and priority boarding dynamically priced by route.
Capture standard public fares alongside discounted v.club member pricing to analyse subscription value propositions.
Parse seat selection payloads to determine exact load factors, remaining premium seats, and standard seat availability.
Extract pricing in MXN, USD, or local currencies based on origin point and point of sale configurations.
Automate searches across 30, 60, or 90 day windows to build comprehensive forward-looking fare curves.
Bypass Akamai and Cloudflare protections common in airline booking engines using residential proxies and TLS fingerprinting.
Brief in. Clean data out.
Provide origin destination pairs, date ranges, and required data points. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for volaris.com.
Schema validation, null rate checks, price outlier detection, and sample payloads before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines deploy aggressive bot mitigation and complex session management. Here is how we maintain reliable extraction.
Volaris uses enterprise bot management. Our infrastructure uses residential proxies with ISP level routing in Mexico and the US, combined with precise TLS fingerprinting to mimic legitimate traveller traffic.
Airline pricing requires maintaining session tokens across multiple API calls. We manage cookies, headers, and dynamic tokens to navigate from search to the ancillary selection pages without dropping the session.
Fares and seat maps are rendered dynamically. We execute full Playwright browser sessions to intercept backend XHR requests, extracting clean JSON directly from the Volaris API responses.
Airlines alter pricing based on the IP location. We route requests through specific geographic nodes to capture accurate point of sale pricing for domestic Mexican and international routes.
Booking engines update frequently. We monitor API schema changes and employ fallback extraction methods to ensure your fare data pipeline does not break during critical pricing windows.
Rival airlines track Volaris base fares, TUA, and ancillary costs to optimise their own yield management and route pricing.
Online travel agencies integrate direct scrape feeds to display accurate ultra low cost carrier pricing where GDS distribution is limited.
Aviation analysts track flight frequencies, aircraft deployment, and estimated load factors to model route profitability.
Revenue management teams use historical fare curves to train machine learning models for predictive pricing.
Financial analysts monitor seat map availability and price elasticity to forecast quarterly passenger volumes.
Tourism boards analyse domestic and cross border flight capacity to predict regional economic impact.
"Volaris fare structures are highly dynamic. Without a dedicated pipeline, tracking their true pricing across base fares, TUA, and ancillaries is impossible."
Aviation data extraction requires navigating aggressive bot protection, complex session states, and multi step booking flows. DataFlirt manages the proxy rotation, JavaScript rendering, and schema maintenance so your revenue management teams receive clean, structured fare matrices ready for immediate analysis.
Everything supported by our volaris.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 manages JavaScript execution, session cookies, and multi step booking navigation.
We maintain pools of residential ISP proxies in Mexico and the US. Rotation happens per request to ensure accurate point of sale pricing and avoid IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for forward looking date scans. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About volaris.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline intercepts the detailed pricing payloads to separate the base fare, the Tarifa de Uso de Aeropuerto, and other government taxes, providing a complete view of the pricing structure.
We utilise residential ISP proxies, primarily routed through Mexico and the US, combined with precise TLS fingerprinting and Playwright browser sessions to mimic legitimate human traffic and bypass Akamai.
Yes. We extract both the standard public fares and the discounted v.club member pricing tiers directly from the search results.
Yes. We navigate the booking flow to the ancillary selection pages to extract dynamic pricing for carry on bags, checked luggage, and specific seat assignments.
We can configure pipelines to scan 30, 60, 90, or up to 330 days in advance depending on your yield management requirements and budget.
Absolutely. We provide a sample run of up to 50 origin destination pairs across a 7 day window to validate schema fit and data quality before contract signing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one off route analysis or a continuous fare monitoring feed across the entire network, we build and operate the infrastructure. Tell us what you need.
