We extract flight schedules, dynamic pricing, baggage fees, hotel rates, and Superapp inventory from AirAsia. 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 airasia.com. All fields typed and schema-versioned.
"flight_number": "AK 511", "origin_code": "KUL", "destination_code": "DMK", "departure_time_local": "2026-08-14T10:30:00", "arrival_time_local": "2026-08-14T11:45:00", "aircraft_type": "Airbus A320", "operated_by": "AirAsia Berhad", "duration_minutes": 135, "stop_count": 0
| # | flight_number | origin_code | destination_code | departure_time_local | arrival_time_local | aircraft_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Live Pricing objects from airasia.com. All fields typed and schema-versioned.
"flight_number": "AK 511", "travel_date": "2026-08-14", "currency": "MYR", "base_fare": 149.0, "taxes_and_fees": 73.0, "total_fare": 222.0, "promo_code_applied": false, "fare_class": "Economy Promo", "seats_remaining": 4
| # | flight_number | travel_date | currency | base_fare | taxes_and_fees | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Fees objects from airasia.com. All fields typed and schema-versioned.
"flight_number": "AK 511", "currency": "MYR", "baggage_20kg_fee": 65.0, "baggage_25kg_fee": 75.0, "seat_selection_standard": 12.0, "seat_selection_hot": 45.0, "meal_fee_average": 15.0, "insurance_fee": 24.0
| # | flight_number | baggage_20kg_fee | baggage_25kg_fee | seat_selection_standard | seat_selection_hot | meal_fee_average |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotel Inventory objects from airasia.com. All fields typed and schema-versioned.
"property_id": "HTL-89412", "property_name": "Tune Hotel KLIA2", "location_city": "Sepang", "star_rating": 3, "room_type": "Double Room", "price_per_night": 180.0, "currency": "MYR", "availability_status": true, "user_rating_score": 7.8
| # | property_id | property_name | location_city | star_rating | room_type | price_per_night |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for MOVE Superapp objects from airasia.com. All fields typed and schema-versioned.
"service_type": "AirAsia Ride", "pickup_location": "KL Sentral", "dropoff_location": "KLCC", "distance_km": 4.2, "estimated_time_mins": 15, "fare_amount": 14.5, "currency": "MYR", "vehicle_type": "Compact", "surge_multiplier": 1.2
| # | service_type | pickup_location | dropoff_location | distance_km | estimated_time_mins | fare_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our AirAsia scraper handles every layer of the platform: flight schedules, dynamic pricing, ancillary fees, and the MOVE Superapp ecosystem - with session management and anti-bot circumvention built in.
Extract origins, destinations, departure times, aircraft models, and layover data across the entire AirAsia network.
Capture base fares, taxes, promotional discounts, and total costs across multiple currencies in real time.
Scrape dynamic pricing for checked baggage, seat selection, in-flight meals, and insurance per flight sector.
Extract bundled flight and hotel package pricing, including accommodation details and room types.
Monitor ride-hailing fares, food delivery menus, and logistics pricing from the AirAsia MOVE ecosystem.
Extract pricing in MYR, THB, IDR, INR, USD, and 15 other currencies for arbitrage analysis.
Track redemption rates and loyalty point requirements for flights and hotel bookings.
Bypass Akamai and Cloudflare protections using residential proxies and TLS fingerprinting.
Run pipelines at hourly intervals and receive only changed prices to minimise storage bloat.
Brief in. Clean data out.
Provide route pairs, dates, hotel locations, or Superapp service areas. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for airasia.com.
Schema validation, null-rate checks, price-outlier detection, and sample routes before full launch.
JSON, CSV, or 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 over DIY.
AirAsia uses aggressive bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass Akamai.
Rather than scraping the DOM, we intercept and parse the underlying GraphQL and REST API responses from the AirAsia frontend, ensuring cleaner data and faster extraction.
Extracting accurate pricing requires maintaining valid session tokens across multiple requests. We handle token refresh cycles and state management automatically.
For large route catalogues, we maintain a hash index of last-seen values per field. 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, price outliers, and coverage drops - and respond before you notice.
Online Travel Agencies monitor AirAsia direct fares to optimise their own markup and discount strategies.
Competing airlines track schedule density and fare pricing on overlapping routes to adjust their own capacity.
Travel aggregators ingest live pricing data to ensure their metasearch results display the most accurate fares.
Aviation analysts track baggage and seat selection fees to understand low-cost carrier monetisation strategies.
Ride-hailing and food delivery competitors monitor AirAsia MOVE pricing and surge multipliers in key Southeast Asian markets.
Hedge funds and tourism boards correlate flight frequency and pricing trends with macroeconomic travel demand.
"AirAsia processes millions of dynamic fare changes daily across its Superapp ecosystem. Accessing this pricing intelligence requires dedicated infrastructure."
Airlines deploy aggressive anti-bot measures to protect their pricing data. Scraping AirAsia requires managing Akamai bot mitigation, handling complex session state for multi-city searches, and parsing highly nested JSON responses from their internal APIs. DataFlirt manages this complexity entirely.
Everything supported by our airasia.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across SEA regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 airasia.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We can configure the pipeline to extract base fares and taxes in MYR, THB, IDR, INR, USD, or any other supported currency for accurate arbitrage analysis.
Yes. We extract ride-hailing fares, food delivery menus, and logistics pricing from the Superapp ecosystem alongside standard flight and hotel data.
We use residential ISP proxies, automated TLS fingerprinting, and request timing modelled on human behaviour to bypass Akamai and Cloudflare protections.
Yes. We parse the dynamic pricing for checked baggage, seat selection, in-flight meals, and insurance per flight sector.
Real-time streaming pipelines achieve sub-15-minute latency for price signals on a defined route set. Full network refreshes complete within a 4-8 hour window.
Absolutely. We provide a sample run of up to 50 route pairs 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 route schedule dump or a continuous price-monitoring feed across 10,000 sectors - we scope, build, and operate the pipeline. Tell us what you need.
