We extract complex flight itineraries, virtual interlining routes, dynamic pricing, and baggage allowances from Kiwi. 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 Itineraries objects from kiwi.com. All fields typed and schema-versioned.
"route_id": "DEL-LHR-1205", "departure_airport": "DEL", "arrival_airport": "LHR", "departure_time": "2026-05-12T08:00:00Z", "arrival_time": "2026-05-12T18:30:00Z", "duration": "10h 30m", "carrier": "Air India", "virtual_interlining": false
| # | route_id | departure_airport | arrival_airport | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from kiwi.com. All fields typed and schema-versioned.
"route_id": "DEL-LHR-1205", "base_price": 450.0, "currency": "USD", "total_price": 520.5, "fare_class": "Economy", "refundable": false, "price_timestamp": "2026-05-10T14:22:00Z"
| # | route_id | base_price | currency | taxes | fees | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage Rules objects from kiwi.com. All fields typed and schema-versioned.
"route_id": "DEL-LHR-1205", "cabin_bag_included": true, "cabin_bag_weight": "7kg", "checked_bag_included": false, "checked_bag_price": 45.0, "personal_item_included": true
| # | route_id | cabin_bag_included | cabin_bag_dimensions | cabin_bag_weight | checked_bag_included | checked_bag_weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Nomad & Multi-City objects from kiwi.com. All fields typed and schema-versioned.
"nomad_id": "NOMAD-8842", "origin": "JFK", "total_duration": "14 days", "total_price": 1250.0, "routing_sequence": "JFK,CDG,FCO,JFK", "carriers_involved": "['Delta', 'Air France', 'ITA']"
| # | nomad_id | origin | destinations | total_duration | total_price | nights_in_destinations |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Carrier & Layover Details objects from kiwi.com. All fields typed and schema-versioned.
"segment_id": "SEG-001", "route_id": "DEL-LHR-1205", "operating_carrier": "Air India", "layover_airport": "FRA", "layover_time": "2h 15m", "self_transfer_required": false, "terminal_change": true
| # | segment_id | route_id | operating_carrier | marketing_carrier | layover_airport | layover_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kiwi scraper traverses complex multi-city routing, virtual interlining matrices, and dynamic fare classes with JavaScript rendering, session management, and anti-bot circumvention built in.
Departure, arrival, flight numbers, aircraft types, and segment-level durations extracted across millions of O&D pairs.
Capture base fares, taxes, hidden fees, and total prices across multiple currencies. Timestamped per crawl.
Extract Kiwi's proprietary self-transfer routes combining non-cooperating carriers into single itineraries.
Cabin bag dimensions, checked baggage costs, and personal item rules extracted per fare class and carrier.
Extract complex multi-city itineraries generated by Kiwi's Nomad algorithm, including layover logic and sequence pricing.
Capture terminal changes, self-transfer requirements, and transit visa warnings for complex connections.
Scrape flexible date grids to identify cheapest departure and return combinations across a 30-day window.
Extract Kiwi Guarantee inclusion status, premium service fees, and disruption protection costs.
Run continuous pipelines at 15-minute or hourly cadences to track volatile airline pricing changes.
Brief in. Clean data out.
Provide O&D pairs, date ranges, or multi-city nodes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for kiwi.com.
Schema validation, null-rate checks, price-outlier detection, and sample itineraries before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel aggregators heavily protect their pricing matrices. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Kiwi utilizes advanced bot protection via Datadome and Cloudflare. Our crawlers use residential ISP proxies with realistic TLS fingerprints, randomised request timing, and full cookie session management.
Kiwi's search results and date matrices are heavily JavaScript-rendered React applications. We run full Playwright browser sessions to trigger lazy-loads and hydrate dynamic pricing widgets.
Extracting Nomad and multi-city itineraries requires maintaining complex session state across multiple search steps. Our pipeline manages these sequential requests without dropping session tokens.
Travel sites change their DOM structure frequently for A/B testing. Our selector strategy uses multiple fallback chains, CSS selectors, XPath, and internal API interception, ensuring pipeline stability.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops, responding before you notice.
Online travel agencies monitor Kiwi's virtual interlining fares to benchmark their own routing algorithms and pricing.
Airlines analyse self-transfer volumes to identify underserved direct routes and optimise network planning.
Metasearch engines integrate scraped Kiwi pricing matrices to offer comprehensive flight comparisons to their users.
Travel operators track competitor price drops in real-time to adjust their own margins and maintain market parity.
Analysts track post-pandemic travel recovery, route popularity, and carrier dominance across specific global corridors.
LLM developers use structured itinerary and pricing datasets to train autonomous travel booking agents.
"Kiwi has mapped the world's most complex self-transfer flight network. Accessing that routing intelligence requires a highly resilient extraction pipeline."
Most teams underestimate the investment required: reliable Kiwi scraping requires residential proxies, full JavaScript rendering for React apps, complex session state management, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our kiwi.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 for Kiwi's React frontend.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required to maintain search context.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 kiwi.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and itinerary information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal data or circumvent authentication walls. Clients should review Kiwi's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. We capture the complete self-transfer logic, including operating carriers, layover durations, terminal changes, and visa requirements for each segment of the itinerary.
Real-time streaming pipelines achieve sub-15-minute latency for price and availability signals on defined O&D pairs. Full matrix refreshes complete within agreed SLA windows.
Yes. We can extract the complex multi-city itineraries generated by Kiwi's Nomad algorithm, including the optimised routing sequence and total duration.
Our smallest packages start at a defined list of O&D pairs (typically 500-10,000 routes) with daily delivery. For larger matrices or continuous polling, we price based on volume and frequency.
Absolutely. We provide a sample run of up to 50 O&D pairs as part of the pre-engagement scoping process, so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily dump of key O&D pairs or a continuous price-monitoring feed across virtual interlining routes, we scope, build, and operate the pipeline. Tell us what you need.
