We extract flight schedules, dynamic fare tiers, ancillary pricing, and seat maps from JetSMART. 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 Flight Schedules objects from jetsmart.com. All fields typed and schema-versioned.
"flight_number": "JA 402", "origin": "SCL", "destination": "LIM", "departure_time": "2026-08-15T08:30:00-04:00", "arrival_time": "2026-08-15T11:20:00-05:00", "duration": "230", "aircraft_type": "Airbus A320neo", "operated_by": "JetSMART Airlines"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare Pricing objects from jetsmart.com. All fields typed and schema-versioned.
"flight_number": "JA 402", "date": "2026-08-15", "currency": "CLP", "base_fare": 45000.0, "taxes": 12500.0, "total_fare": 57500.0, "fare_class": "Vuela Ligero", "available_seats": 7
| # | flight_number | date | currency | base_fare | taxes | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from jetsmart.com. All fields typed and schema-versioned.
"flight_number": "JA 402", "date": "2026-08-15", "carry_on_bag_fee": 15000.0, "checked_bag_fee": 22000.0, "standard_seat_fee": 4500.0, "premium_seat_fee": 12000.0, "currency": "CLP"
| # | flight_number | date | carry_on_bag_fee | checked_bag_fee | priority_boarding_fee | pet_in_cabin_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from jetsmart.com. All fields typed and schema-versioned.
"origin_code": "SCL", "origin_city": "Santiago", "destination_code": "LIM", "destination_city": "Lima", "country": "Peru", "direct_flight": true, "active_status": true
| # | origin_code | origin_city | destination_code | destination_city | country | direct_flight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Discount Club objects from jetsmart.com. All fields typed and schema-versioned.
"flight_number": "JA 402", "date": "2026-08-15", "standard_fare": 57500.0, "club_fare": 49500.0, "club_discount_pct": 14, "subscription_tier": "Standard", "currency": "CLP", "scraped_at": "2026-05-12T09:14:33Z"
| # | flight_number | date | standard_fare | club_fare | club_discount_pct | club_baggage_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our JetSMART scraper handles session-based booking flows, geo-targeted pricing, and dynamic calendar rendering. We bypass airline bot protection to deliver clean fare and schedule data.
Extract departure times, arrival times, aircraft types, and flight durations across the entire JetSMART network.
Capture base fares, taxes, and total prices for all fare classes including Vuela Ligero and Vuela Seguro.
Monitor dynamic pricing for carry-on bags, checked luggage, seat selection, and priority boarding.
Use region-specific residential proxies to capture fares as they appear to users in Chile, Argentina, Peru, or Colombia.
Extract multi-day fare matrices from the calendar view to track price fluctuations over time.
Capture member-only fares and baggage discounts available through the JetSMART Discount Club.
Extract fares in CLP, ARS, PEN, COP, or USD based on the point of sale and requested currency.
Track active routes, frequency changes, and new destination launches across the network.
Run pipelines at hourly or daily cadences to track volatile pricing changes leading up to departure dates.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and target currencies. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, session management, and anti-bot handling for jetsmart.com.
Schema validation, null-rate checks, tax calculation verification, and currency normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure.
JetSMART uses advanced bot mitigation to block automated searches. Our crawlers use LATAM residential proxies with realistic browser fingerprints and full cookie session management to blend in with legitimate passenger traffic.
Flight data is locked behind sequential search forms. We maintain stateful Playwright sessions that navigate the origin, destination, and date selection flows to reach the final pricing matrix.
Fares and ancillary fees are loaded dynamically via JavaScript after the initial page load. We execute full browser sessions to hydrate these widgets and capture the exact prices displayed to users.
Airlines display different prices based on the user location. We route requests through specific country proxies (Chile, Argentina, Peru) to capture accurate local market pricing.
JetSMART splits fares into base price, boarding taxes, and optional fees. We parse these complex DOM structures into a clean, normalised schema where every cost component is isolated.
Airlines and OTAs monitor JetSMART fares on competing routes to adjust their own yield management and pricing strategies.
Travel agencies verify that the fares displayed on their platforms match the direct-booking prices on jetsmart.com.
Aviation analysts track route expansions, frequency changes, and capacity deployment across the South American market.
Pricing teams analyse how JetSMART structures baggage and seat fees to optimise their own unbundled fare products.
Meta-search engines ingest schedule and pricing data to provide comprehensive flight options to consumers.
Revenue management teams correlate price fluctuations and seat availability with broader market demand trends.
"JetSMART dynamic pricing engine updates fares constantly based on demand and origin IP. Capturing this requires session-aware scraping infrastructure."
Airlines deploy aggressive anti-bot measures to protect their pricing data. Scraping JetSMART requires rotating residential proxies, handling complex multi-step search sessions, and parsing dynamic JavaScript payloads. DataFlirt manages this infrastructure so you can focus on yield management and competitor analysis.
Everything supported by our jetsmart.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.
Playwright handles complex multi-step booking flows, maintaining cookie state and session variables required to reach final pricing pages.
We route requests through LATAM residential proxies to bypass geographical restrictions and capture accurate local market pricing.
Pipelines run on scalable cloud infrastructure managed by Airflow, ensuring high-frequency price updates are delivered on time.
Data delivered to where your team already works — no new tooling required.
About jetsmart.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not extract personal passenger data or bypass authentication walls. Clients should review airline terms of service and consult legal counsel for specific use cases.
We use LATAM residential proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. This allows us to navigate booking flows without triggering security blocks.
Yes. We route requests through specific country proxies to capture fares in CLP, ARS, PEN, COP, or USD as they appear to local users.
Yes. We parse the dynamic unbundled pricing for carry-on bags, checked luggage, seat selection, and priority boarding.
We can configure pipelines to run at hourly or daily cadences depending on your requirements for price volatility tracking.
Our packages typically start at a defined list of origin-destination pairs with daily delivery. We price based on route volume and delivery frequency.
Yes. We provide a sample run for specific routes to validate schema fit 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 schedule dump or continuous price monitoring across the network, we scope, build, and operate the pipeline. Tell us what you need.
