We extract flight schedules, dynamic fare matrices, seat availability, and route network updates from Akasa Air. 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 akasaair.com. All fields typed and schema-versioned.
"flight_number": "QP 1101", "origin": "BLR", "destination": "BOM", "departure_time": "07:30", "arrival_time": "09:15", "duration_minutes": 105, "aircraft_type": "Boeing 737 MAX 8"
| # | flight_number | origin | destination | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Live Pricing objects from akasaair.com. All fields typed and schema-versioned.
"flight_number": "QP 1101", "fare_class": "Saver", "total_fare": 4599.0, "taxes": 750.0, "currency": "INR", "seats_remaining": 4, "search_date": "2026-10-25T08:00:00Z"
| # | flight_number | search_date | departure_date | fare_class | base_fare | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Network objects from akasaair.com. All fields typed and schema-versioned.
"origin_code": "BLR", "destination_code": "DEL", "active_status": true, "frequency_per_week": 14, "direct_flight": true, "distance_km": 1740
| # | origin_code | destination_code | distance_km | active_status | frequency_per_week | first_flight_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Flight Status objects from akasaair.com. All fields typed and schema-versioned.
"flight_number": "QP 1322", "status": "Delayed", "delay_minutes": 45, "scheduled_departure": "14:00", "estimated_departure": "14:45", "arrival_terminal": "T2"
| # | flight_number | target_date | scheduled_departure | estimated_departure | status | departure_gate |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Ancillary Services objects from akasaair.com. All fields typed and schema-versioned.
"flight_number": "QP 1101", "seat_selection_fee_min": 150.0, "seat_selection_fee_max": 1500.0, "extra_baggage_fee_per_kg": 500.0, "priority_checkin_fee": 400.0, "lounge_access_available": false
| # | flight_number | meal_options | seat_selection_fee_min | seat_selection_fee_max | extra_baggage_fee_per_kg | priority_checkin_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Akasa Air scraper handles every layer of the booking platform: flight schedules, dynamic pricing matrices, ancillary fees, and route updates with full session management and anti-bot circumvention built in.
Flight numbers, departure and arrival times, aircraft types, and terminal information mapped across the entire active network.
Extract base fares, taxes, and total prices across all fare classes including Saver and Flexi tiers, timestamped per crawl.
Poll multi-day fare calendars to identify pricing trends and cheapest travel dates for specific route combinations.
Monitor 'seats remaining' warnings and inventory depth indicators to estimate flight load factors.
Capture pricing for seat selection, extra baggage, priority boarding, and in-flight meals tied to specific flights.
Track new route launches, frequency changes, and seasonal cancellations across domestic and international sectors.
Monitor real-time flight status, estimated departure times, and delay minutes directly from the operational dashboard.
Isolate base fares from UDF, PSF, and GST components to normalise pricing across different airports and sectors.
Run one-off schedule exports or configure continuous pipelines at hourly cadences with change-detection diffing.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or specific flight numbers. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session token management, and payload construction for akasaair.com.
Schema validation, null-rate checks, fare-outlier detection, and sample matrix searches before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines enforce strict rate limits and session timeouts. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Akasa Air search endpoints require cryptographic session tokens and dynamic JSON payloads. We reverse-engineer the client-side token generation to construct valid API requests without constantly loading the heavy frontend SPA.
Airline APIs aggressively rate-limit IPs that poll fare matrices too frequently. We distribute search requests across thousands of Indian residential IPs, ensuring our polling volume remains below detection thresholds.
Flight search sessions expire quickly to prevent inventory holding. Our crawlers detect session timeouts immediately and re-initialise state, ensuring long-running matrix scrapes complete without partial data loss.
Booking engine API contracts change during platform updates. We monitor the JSON response structures and apply schema validation on every run, alerting on missing fields rather than silently dropping data.
For high-frequency fare monitoring, we maintain a hash index of last-seen prices per flight and date. Subsequent runs only push diffs, reducing downstream processing load and storage bloat.
Online travel agencies ingest direct pricing to cross-reference against GDS feeds and display accurate fares to end users.
Rival airlines monitor Akasa Air pricing across overlapping sectors to adjust their own yield management algorithms.
Aviation analysts track flight frequencies, aircraft utilisation, and route expansions to model airline profitability.
Consultancies analyse domestic aviation capacity, seat availability trends, and fare elasticity across key metro routes.
Insurtech platforms monitor real-time flight status and historical delay data to price flight cancellation and delay insurance.
Pricing teams use historical fare matrices to train machine learning models predicting future airline ticket costs.
"Airline pricing is the original high-frequency trading market. Extracting Akasa Air fares requires continuous polling across thousands of route and date combinations."
Most teams underestimate the infrastructure required to scrape flight data. Airline booking engines enforce strict rate limits, complex token exchanges, and rapid session timeouts. DataFlirt handles the proxy rotation, session state, and payload construction so your engineers can focus on yield analysis, not infrastructure maintenance.
Everything supported by our akasaair.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 search orchestration and concurrency. Playwright executes the initial token generation and session establishment required by the booking engine.
We maintain large pools of Indian residential proxies to distribute search volume and avoid booking engine rate limits.
Pipelines run on AWS Lambda for burst scaling during matrix searches. Airflow handles scheduling and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About akasaair.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt targets only public, non-authenticated search interfaces. We do not extract personal passenger data, PNR details, or violate data privacy laws. Clients should review airline terms of service and consult legal counsel for specific commercial use cases.
We use distributed Indian residential proxies and manage search request timing to mimic human behaviour. We also reverse-engineer client-side token generation to minimise unnecessary requests to the frontend SPA.
Yes. We can configure pipelines to query point-to-point, return, and multi-city itineraries based on your specific origin and destination requirements.
For continuous monitoring on targeted routes, we can achieve sub-15-minute latency. Full network matrix scans across a 90-day forward window typically complete within a 4 to 8 hour cycle depending on concurrency settings.
Yes. We extract the full fare breakdown including base fare, User Development Fee (UDF), Passenger Service Fee (PSF), Aviation Security Fee (ASF), and GST components where surfaced by the booking engine.
Our smallest packages start at a defined route list (typically 50 to 200 origin-destination pairs) with daily forward-looking matrix scans. Contact us with your route and frequency requirements for a scoped quote.
Absolutely. We provide a sample run of up to 10 routes across a 7-day forward window as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily network schedule dump or continuous fare monitoring across specific sectors, we scope, build, and operate the pipeline. Tell us what you need.
