We extract flight schedules, dynamic pricing, LATAM Pass points, cabin availability, and route metadata. 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 latamairlines.com. All fields typed and schema-versioned.
"flight_number": "LA800", "origin": "SCL", "destination": "SYD", "departure_time": "2024-08-12T23:55:00Z", "duration": "14h 20m", "stops": 0, "aircraft_type": "Boeing 787-9"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from latamairlines.com. All fields typed and schema-versioned.
"flight_id": "LA800_SCL_SYD_20240812", "cabin_class": "Premium Economy", "fare_brand": "Plus", "total_price": 1450.0, "currency": "USD", "seats_remaining": 4, "scraped_at": "2024-05-12T09:14:00Z"
| # | flight_id | cabin_class | fare_brand | base_price | taxes | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Intelligence objects from latamairlines.com. All fields typed and schema-versioned.
"origin_code": "GRU", "destination_code": "MIA", "route_type": "International", "operating_days": "['Mon', 'Wed', 'Fri']", "codeshare_partners": "['Delta Air Lines']", "average_delay_mins": 15
| # | origin_code | destination_code | distance_km | route_type | operating_days | codeshare_partners |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Baggage & Ancillaries objects from latamairlines.com. All fields typed and schema-versioned.
"fare_brand": "Light", "cabin_bags_allowed": 1, "checked_bags_allowed": 0, "seat_selection_fee": 15.0, "change_fee": 100.0, "cancellation_fee": "None"
| # | fare_brand | cabin_bags_allowed | checked_bags_allowed | checked_bag_weight_kg | seat_selection_fee | change_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for LATAM Pass Points objects from latamairlines.com. All fields typed and schema-versioned.
"flight_number": "LA800", "origin": "SCL", "destination": "SYD", "points_required": 120000, "taxes_cash": 85.5, "currency": "USD", "redemption_tier": "Standard"
| # | flight_number | origin | destination | cabin_class | points_required | taxes_cash |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our LATAM scraper handles every layer of the booking engine: flight schedules, dynamic pricing matrices, LATAM Pass redemption rates, and ancillary fees. We manage the session cookies and anti-bot systems automatically.
Extract Base, Plus, Top fares across Economy, Premium Economy, and Premium Business cabins.
Track points required and cash tax components for reward flights across all routes.
Monitor low-stock warnings and block-seat inventory signals per cabin class.
Parse complex layovers, connection times, and mixed-carrier codeshare flights.
Capture baggage allowances, seat selection fees, and cancellation policies per fare tier.
Extract equipment types, operating carriers, and Wi-Fi availability flags.
Extract native currencies and normalise pricing schemas across LATAM regional domains.
Query critical routes at sub-hourly intervals to capture dynamic yield management shifts.
Bypass Akamai and Datadome protections using residential IPs and TLS fingerprinting.
Brief in. Clean data out.
Provide O&D pairs, date ranges, and cabin classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for latamairlines.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines aggressively protect pricing data to prevent OTA scraping. Here is how we maintain steady extraction rates.
LATAM relies on strict WAF rules. We spoof TLS signatures and manage session cookies to mimic legitimate browser behaviour.
Instead of parsing brittle HTML, we intercept the underlying GraphQL and REST responses powering the flight search interface.
Flight prices vary by origin IP. We route requests through residential nodes in Brazil, Chile, or the US to capture localised pricing.
LATAM booking flows require stateful tokens. Our Playwright scripts handle the full sequence from origin selection to fare display.
We distribute requests across thousands of IPs with randomised delays to stay below volumetric rate limits.
OTAs and rival airlines track LATAM fare adjustments on overlapping routes to optimise their own yield management.
Metasearch engines ingest schedule and pricing data to provide comprehensive flight comparisons.
Analysts track LATAM Pass redemption rates against cash fares to calculate point valuations over time.
Aviation consultants monitor flight frequencies, aircraft types, and sold-out cabins to estimate route performance.
Enterprise travel managers track historical pricing trends to negotiate better corporate discount rates.
Tour operators combine real-time LATAM flight data with hotel inventory to sell dynamic holiday packages.
"Flight pricing is the ultimate dynamic dataset. LATAM adjusts fares constantly based on yield curves - capturing this requires infrastructure that never sleeps."
Airline websites deploy aggressive bot mitigation to protect their inventory data. Reliable extraction from latamairlines.com requires managing stateful booking flows, intercepting encrypted API traffic, and rotating residential proxies across South American regions. DataFlirt handles this complexity so your analysts can focus on pricing intelligence.
Everything supported by our latamairlines.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 routes capture raw JSON responses from LATAM backend, bypassing brittle DOM parsing and reducing payload size.
We route requests through ISP proxies in specific Latin American countries to ensure accurate local pricing and avoid geo-blocks.
Airflow schedules polling intervals per route, while AWS Lambda handles burst capacity during high-frequency fare tracking.
Data delivered to where your team already works — no new tooling required.
About latamairlines.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We can target specific regional domains or use geo-targeted proxies to extract fares in BRL, CLP, PEN, USD, or EUR.
We use Playwright to manage TLS fingerprints and execute required JavaScript challenges, backed by residential proxies to distribute request volume.
Yes. We extract the points required, the cash tax component, and the availability status for reward bookings across all cabin classes.
For critical O&D pairs, we can configure pipelines to poll at sub-hourly intervals to capture dynamic yield management adjustments.
Yes. We capture the rules and fees associated with each fare brand including baggage allowances and seat selection costs.
Provide a list of Origin-Destination airport codes, date ranges, and desired cabin classes. We handle the search permutations.
No. We only extract publicly available flight schedules, pricing, and availability. We do not extract PII or authenticated booking data.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily schedule updates or high-frequency fare monitoring across thousands of routes - we scope, build, and operate the pipeline. Tell us what you need.
