We extract flight schedules, dynamic pricing, fare families, seat availability, and ancillary fees from Air Serbia. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 airserbia.com. All fields typed and schema-versioned.
"flight_number": "JU314", "origin": "BEG", "destination": "CDG", "departure_time": "2026-08-14T06:45:00Z", "arrival_time": "2026-08-14T09:20:00Z", "duration_minutes": 155, "operating_carrier": "Air Serbia", "stops": 0
| # | flight_number | origin | destination | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Fares objects from airserbia.com. All fields typed and schema-versioned.
"flight_number": "JU314", "departure_date": "2026-08-14", "fare_family": "Economy Light", "total_price": 142.5, "base_fare": 95.0, "tax_amount": 47.5, "currency": "EUR", "seats_remaining": 4
| # | flight_number | departure_date | fare_family | fare_basis | total_price | base_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Fees objects from airserbia.com. All fields typed and schema-versioned.
"flight_number": "JU314", "bag_type": "Checked Baggage", "bag_weight_kg": 23, "bag_price": 35.0, "seat_type": "Extra Legroom", "seat_price": 25.0, "currency": "EUR"
| # | flight_number | bag_type | bag_weight_kg | bag_price | seat_type | seat_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Route Network objects from airserbia.com. All fields typed and schema-versioned.
"origin_airport": "BEG", "destination_airport": "JFK", "distance_km": 7245, "frequency_weekly": 7, "seasonal": false, "direct_flight": true
| # | origin_airport | destination_airport | distance_km | frequency_weekly | seasonal | start_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fleet Data objects from airserbia.com. All fields typed and schema-versioned.
"aircraft_code": "332", "aircraft_model": "A330-200", "manufacturer": "Airbus", "total_seats": 268, "business_seats": 21, "wifi_available": true, "power_outlets": true
| # | aircraft_code | aircraft_model | manufacturer | total_seats | economy_seats | business_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our scraper navigates the Air Serbia booking engine, handling session tokens, dynamic fare matrices, and multi-currency pricing arrays without triggering bot protections.
Extract departure times, arrival times, aircraft types, and flight durations across the entire Air Serbia network.
Capture pricing for Economy Light, Standard, Comfort, and Business classes simultaneously for any given flight.
Monitor remaining seat counts per fare class to model load factors and demand curves.
Extract dynamic pricing for extra baggage, seat selection, and priority boarding options.
Identify operating carriers versus marketing carriers for all partner flights listed on the platform.
Extract base fares and taxes in RSD, EUR, USD, or any other supported currency.
Pull flexible date grids to capture pricing trends across +/- 3 day windows in a single search.
Construct and scrape complex multi-segment journeys to analyse pricing anomalies.
Run intra-day pipelines to capture dynamic pricing adjustments and flash sales.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and required fare families. We design the extraction schema.
We configure Playwright crawlers, manage session state, and implement IP rotation specific to airline booking engines.
Schema validation, tax calculation checks, and currency normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or warehouse on agreed cadence.
Airline websites use complex session management and aggressive rate limiting. Here is how we maintain stable extraction.
Booking engines require a strict sequence of HTTP requests. We maintain full session state, passing required CSRF tokens and cookies through the search funnel to access the final fare matrix.
Flight results load asynchronously via JavaScript. We use headless browsers to execute the frontend code, wait for XHR responses, and parse the resulting JSON payloads directly from network traffic.
Airlines limit searches per IP to block OTAs and competitors. We distribute requests across thousands of European residential IPs, keeping query volume per node well below detection thresholds.
We normalise airport codes, aircraft types, and currency values, splitting total prices into base fares and tax components to ensure your downstream models receive clean data.
When the booking engine returns temporary errors or timeouts under heavy load, our pipeline automatically cycles the proxy and retries the specific date-route combination without failing the entire batch.
Online travel agencies ingest direct pricing to verify GDS parity and identify direct-booking discounts.
Rival airlines track Air Serbia pricing on contested routes to adjust their own revenue management algorithms.
Aviation analysts track network expansion, frequency changes, and seasonal route deployments.
Data science teams correlate seat availability drops with price increases to reverse-engineer yield management strategies.
Travel management companies monitor schedule changes and cancellations to proactively rebook corporate clients.
Consultancies track the unbundling of fares and dynamic pricing of baggage to benchmark industry trends.
"Airline pricing is highly dynamic and heavily cached. Querying live inventory and fare families requires strict session management and persistent IP rotation."
Airlines deploy aggressive rate limiting and complex session structures to protect their booking engines. Scraping Air Serbia requires full browser rendering to navigate the multi-step search flow, parse dynamic fare matrices, and extract accurate seat availability without triggering bot mitigations. We manage this infrastructure entirely.
Everything supported by our airserbia.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 booking flows, managing CSRF tokens and session cookies required to access final pricing matrices.
Requests are routed through European residential IPs to avoid data centre IP bans commonly deployed by airline IT systems.
Pipelines execute on Kubernetes clusters, allowing massive parallelisation of date and route queries to complete network scans quickly.
Data delivered to where your team already works — no new tooling required.
About airserbia.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public flight schedules and pricing is generally permissible for non-disruptive use cases. DataFlirt extracts only publicly available inventory without bypassing authentication walls. Clients must ensure their specific use case complies with applicable terms and regulations.
We utilise large pools of residential proxies and mimic human search behaviour. By distributing queries and maintaining proper session states, we keep request rates per IP well below typical blocking thresholds.
Yes. We can scrape pricing and schedules for the entire Air Serbia network, including code-share flights operated by partner airlines.
Pipelines can be configured to run at custom intervals. High-priority routes can be polled multiple times per day, while full network scans typically run daily.
Yes. Every pipeline run is timestamped. We deliver the current snapshot, and you can build historical time-series data in your warehouse to model pricing curves.
We typically start with a defined set of origin-destination pairs and date ranges. Contact us with your specific route volume and frequency requirements for a quote.
Yes. We navigate the booking flow to extract dynamic pricing for baggage, seat selection, and other add-ons specific to the selected fare family.
Yes. We offer a sample extraction for a small subset of routes and dates to validate schema compatibility before you commit to a full pipeline.
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 build and maintain the infrastructure.
