We extract flight schedules, dynamic fare classes, seat maps, and route availability from KLM. 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 klm.com. All fields typed and schema-versioned.
"flight_number": "KL601", "origin": "AMS", "destination": "LAX", "departure_time": "2026-05-14T09:50:00Z", "arrival_time": "2026-05-14T11:50:00Z", "duration_minutes": 660, "aircraft_type": "Boeing 787-10"
| # | flight_number | origin | destination | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Fares objects from klm.com. All fields typed and schema-versioned.
"flight_number": "KL601", "cabin_class": "Economy", "fare_type": "Standard", "price": 849.0, "currency": "EUR", "flying_blue_miles": 3400, "seats_remaining": 4
| # | flight_number | departure_date | cabin_class | fare_type | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Seat Maps & Availability objects from klm.com. All fields typed and schema-versioned.
"flight_number": "KL601", "cabin_class": "World Business Class", "total_seats": 38, "available_seats": 12, "occupied_seats": 26, "business_class_price": 3299.0
| # | flight_number | date | cabin_class | total_seats | available_seats | occupied_seats |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Route Network objects from klm.com. All fields typed and schema-versioned.
"origin_airport": "AMS", "dest_airport": "NRT", "direct_flight": true, "frequency_per_week": 7, "distance_km": 9300, "alliance_partners": "['Air France', 'Delta']"
| # | origin_airport | dest_airport | direct_flight | frequency_per_week | first_departure | last_departure |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Baggage & Ancillaries objects from klm.com. All fields typed and schema-versioned.
"flight_number": "KL601", "fare_type": "Light", "checked_bags_allowed": 0, "cabin_bags_allowed": 1, "extra_bag_price": 60.0, "wifi_available": true
| # | flight_number | fare_type | checked_bags_allowed | cabin_bags_allowed | max_weight_kg | extra_bag_price |
|---|---|---|---|---|---|---|
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Our KLM scraper handles the entire booking flow: dynamic pricing matrices, multi-city route networks, and seat availability — bypassing Akamai bot protection and complex session states.
Departure, arrival, block time, aircraft equipment, and operating carrier data across the entire KLM and SkyTeam network.
Capture Light, Standard, and Flex fare tiers across Economy, Premium Comfort, and World Business Class. Timestamped per crawl.
Extract miles required for reward flights and miles earned per cash fare, mapping loyalty program value against standard pricing.
Determine load factors by scraping available vs occupied seats, including Economy Comfort and extra legroom upgrade fees.
Identify true operating carriers for Air France, Delta, and Virgin Atlantic code-shares sold through the KLM storefront.
Set Point of Sale (POS) parameters to extract localised pricing in EUR, USD, GBP, and other currencies to detect geo-arbitrage.
Track extra baggage costs, lounge access fees, and paid meal options tied to specific fare classes.
Map specific tail numbers or equipment types (e.g., Boeing 777-300ER, Embraer 195-E2) to specific routes and schedules.
Run batch extractions 90 days out, or configure continuous pipelines for high-frequency price monitoring on competitive routes.
Brief in. Clean data out.
Provide O&D (Origin & Destination) pairs, date ranges, and cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for klm.com.
Schema validation, null-rate checks, price-outlier detection, and schedule verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines employ aggressive bot mitigation to protect pricing data. Here's how our infrastructure maintains continuous access.
KLM relies on Akamai to block automated traffic. We use residential European proxies, clean TLS fingerprints, and humanised interaction delays to bypass rate limits and maintain high success rates.
Flight searches require maintaining stateful sessions across multiple XHR requests. Our Playwright orchestrator manages cookies, local storage, and hidden tokens to progress through the search funnel without dropping context.
Fare calendars and multi-tier pricing grids are rendered client-side. We execute JavaScript fully to capture the final DOM state, ensuring we extract the exact prices displayed to users.
Airlines adjust inventory constantly. We maintain a state hash of previously seen fare classes and prices, emitting diffs only when inventory buckets open or close, saving you downstream processing.
If KLM alters its search API or DOM structure, our Prometheus and Grafana stack flags the schema drift immediately. We deploy selector updates before your downstream models consume bad data.
OTAs and competing airlines monitor KLM's fare buckets and dynamic pricing to adjust their own revenue management algorithms.
Aviation analysts track schedule changes, frequency adjustments, and equipment downgrades to gauge route profitability.
Points aggregators extract Flying Blue redemption rates to calculate cent-per-mile valuations and alert users to reward availability.
Metasearch engines enrich their caching layers with direct-from-carrier pricing to reduce GDS query costs.
Hedge funds and PE firms correlate seat map load factors with macroeconomic trends to forecast airline quarterly revenue.
Ancillary revenue teams analyse KLM's pricing for extra baggage, seat selection, and Wi-Fi to optimise their own fee structures.
"Airline pricing is the original dynamic market. Without programmatic access to fare buckets and seat maps, revenue management is just guesswork."
Extracting data from major airlines requires navigating complex booking flows, aggressive bot mitigation, and highly dynamic frontend architectures. DataFlirt manages the residential proxy rotation, session handling, and selector maintenance so your data science team can focus on yield analysis — not infrastructure troubleshooting.
Everything supported by our klm.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 manages request queues and concurrency. Playwright executes the complex XHR sequences required to generate KLM flight search results and seat maps.
We route requests through ISP-grade residential IP pools in the EU to match expected Point of Sale behaviour and bypass Akamai rate limits.
Pipelines are deployed on Kubernetes with Airflow managing schedule dependencies. Postgres stores state hashes for incremental diff delivery.
Data delivered to where your team already works — no new tooling required.
About klm.com scraping, legality, and pipeline operations.
Ask us directly →Extracting public fare and schedule data is generally permissible under applicable law. DataFlirt targets only unauthenticated, publicly accessible search flows. We do not bypass login gates or extract personally identifiable information (PII). Clients should consult their legal counsel regarding specific commercial use cases.
We utilise residential proxies, TLS fingerprint spoofing, and human-like interaction patterns via Playwright to ensure our requests resemble legitimate user traffic, preventing IP bans and CAPTCHA loops.
Yes. We can extract the miles required and associated cash taxes for reward flights, allowing you to monitor redemption availability across specific routes.
For targeted O&D pairs, we can run continuous pipelines achieving sub-hourly updates. Broader network scans are typically executed daily to balance compute costs with data freshness.
Yes. We parse the seat selection flow to determine total capacity, occupied seats, and available seats, including premium options like Economy Comfort.
Our schema fully supports multi-segment flights, capturing stopovers, connection times, and operating carriers for each leg of the journey.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily schedule updates or high-frequency fare monitoring across key routes — we scope, build, and operate the pipeline. Tell us what you need.
