We extract route schedules, dynamic fare pricing, bundle costs, baggage fees, and seating charts from Allegiant Air. Delivered as clean JSON, CSV, or Parquet to your preferred data lake on 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 allegiantair.com. All fields typed and schema-versioned.
"flight_number": "G4 102", "origin_iata": "LAS", "destination_iata": "ATW", "departure_time": "2026-08-14T08:30:00Z", "arrival_time": "2026-08-14T13:45:00Z", "duration_minutes": 195, "aircraft_type": "Airbus A320", "stops": 0
| # | flight_number | origin_iata | destination_iata | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fare Pricing objects from allegiantair.com. All fields typed and schema-versioned.
"flight_id": "G4102_LAS_ATW_20260814", "base_fare": 48.5, "taxes": 14.2, "total_price": 62.7, "currency": "USD", "fare_class": "Standard", "travel_date": "2026-08-14", "scrape_timestamp": "2026-05-12T10:15:22Z"
| # | flight_id | base_fare | taxes | total_price | currency | fare_class |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Fees objects from allegiantair.com. All fields typed and schema-versioned.
"flight_id": "G4102_LAS_ATW_20260814", "carry_on_fee": 35.0, "checked_bag_fee": 40.0, "seat_selection_min": 12.0, "seat_selection_max": 45.0, "priority_boarding_fee": 15.0, "bundle_bonus": 65.0
| # | flight_id | carry_on_fee | checked_bag_fee | seat_selection_min | seat_selection_max | priority_boarding_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from allegiantair.com. All fields typed and schema-versioned.
"flight_id": "G4102_LAS_ATW_20260814", "total_seats": 177, "available_seats": 42, "booked_seats": 135, "extra_legroom_available": 4, "exit_row_available": 2, "standard_seats_available": 36, "last_updated": "2026-05-12T10:15:25Z"
| # | flight_id | total_seats | available_seats | booked_seats | extra_legroom_available | exit_row_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Bundled Packages objects from allegiantair.com. All fields typed and schema-versioned.
"package_id": "PKG_LAS_ATW_4N", "flight_id": "G4102_LAS_ATW_20260814", "hotel_included": true, "car_rental_included": false, "package_price": 450.0, "original_price": 520.0, "savings": 70.0, "provider_name": "MGM Grand"
| # | package_id | flight_id | hotel_included | car_rental_included | package_price | original_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Allegiant Air scraper navigates stateful booking flows to extract unbundled fares, hidden ancillary fees, and dynamic pricing metrics with automated bot circumvention.
Capture base fares, taxes, and mandatory surcharges across all origin and destination pairs on the network.
Extract dynamic pricing for carry-on bags, checked luggage, and priority boarding based on route and booking window.
Monitor active routes, seasonal additions, and frequency changes across the entire point-to-point network.
Parse interactive seat maps to determine load factors, premium seat pricing, and exit row availability.
Track flight plus hotel or car rental package pricing, including third-party provider details and advertised savings.
Extract multi-day calendar views to identify lowest fare dates and track pricing elasticity over time.
Run high-frequency checks on specific routes to monitor inventory depletion and yield management adjustments.
Bypass Akamai and Cloudflare protections using residential proxies and TLS fingerprint spoofing.
Maintain complex booking session states required to reach deep ancillary and payment summary pages.
Brief in. Clean data out.
Provide origin-destination pairs, travel date ranges, and required data depth. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, session management, and bot mitigation for allegiantair.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 rely on complex session states. Here is how we maintain reliable extraction.
Airline sites use strict edge protection. Our crawlers use US residential ISP proxies with realistic browser fingerprints, randomised request timing, and TLS spoofing to bypass these checks reliably.
To extract baggage fees and seat maps, the scraper must progress through the booking funnel. We maintain strict cookie and session state across multiple page loads to reach deep data.
Fares change based on search volume and inventory. Our infrastructure normalises these fluctuations by running concurrent checks and isolating cache anomalies to deliver accurate pricing signals.
Allegiant Air restricts access from non-US IP addresses. We strictly route all traffic through high-reputation US residential nodes to prevent geo-blocking and IP bans.
Booking engine DOMs update frequently to support new marketing initiatives. Our selector strategy uses fallback chains and API interception where possible to ensure your data pipeline remains stable.
Rival airlines track base fares and ancillary fees on overlapping routes to adjust their own pricing algorithms.
Aviation analysts monitor schedule frequency and load factors to estimate route profitability and network strategy.
Financial analysts track the unbundled fee structures to model total revenue per available seat mile.
Online travel agencies integrate direct scrape feeds to offer complete fare comparisons where API access is restricted.
Tourism boards track incoming flight capacity and package pricing to forecast regional visitor volumes.
Data science teams use historical fare matrices to train predictive models for travel demand and price elasticity.
"Allegiant operates a highly dynamic ultra-low-cost model where base fares are just the beginning. Tracking the true cost requires parsing every ancillary fee layer."
Extracting data from airline booking engines requires navigating strict bot protection, stateful session flows, and complex Javascript rendering. DataFlirt handles the proxy rotation, session persistence, and schema maintenance so your analysts can focus on yield management and competitor benchmarking.
Everything supported by our allegiantair.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 handles JavaScript rendering, cookie sessions, and interaction flows required for booking engines.
We maintain pools of US residential ISP proxies. Rotation happens per session to maintain the stateful flows required to reach ancillary fee pages.
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 allegiantair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and schedule data is generally permissible. We target only public, non-authenticated routes and fares. 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 US residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and strict session management. We monitor for block rates in real time and rotate IP pools automatically to maintain pipeline health.
For monitored routes, we can achieve sub-60-minute latency. Full network refreshes typically run daily. Historical snapshots are available from the day your pipeline is commissioned.
Yes. We progress through the booking funnel to extract dynamic ancillary fees, including carry-on bags, checked bags, priority boarding, and specific seat map pricing.
Our smallest packages start at a defined list of origin-destination pairs with daily delivery. For full network monitoring or high-frequency intra-day checks, we price based on compute volume.
Yes. We provide a sample run of up to 50 routes 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 daily schedule updates or high-frequency fare monitoring across the network, we scope, build, and operate the pipeline. Tell us what you need.
