We extract vehicle availability, base rates, insurance tiers, branch locations, and Sixt+ subscription data. 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 Car Rentals objects from sixt.com. All fields typed and schema-versioned.
"branch_id": "SFO01", "vehicle_class": "Premium SUV", "acriss_code": "PFAR", "make_model": "BMW X5 or similar", "base_price": 145.5, "currency": "USD", "pay_now_price": 131.0, "availability_status": true
| # | quote_id | branch_id | pickup_location | dropoff_location | pickup_date | dropoff_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fleet Metadata objects from sixt.com. All fields typed and schema-versioned.
"acriss_code": "PFAR", "category": "Premium", "transmission": "Automatic", "fuel_type": "Petrol", "seats": 5, "doors": 4, "luggage_capacity": 4, "minimum_age": 25
| # | acriss_code | category | transmission | fuel_type | seats | doors |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Branch Locations objects from sixt.com. All fields typed and schema-versioned.
"branch_id": "LHR01", "branch_name": "London Heathrow Airport", "city": "London", "country": "UK", "airport_branch": true, "return_24h": true, "latitude": 51.47, "longitude": -0.4543
| # | branch_id | branch_name | address_line1 | city | country | postal_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Insurance & Extras objects from sixt.com. All fields typed and schema-versioned.
"quote_id": "Q-98421", "protection_package": "Premium", "deductible_amount": 0.0, "daily_rate": 34.99, "currency": "USD", "gps_daily_rate": 12.5, "additional_driver_rate": 15.0
| # | quote_id | protection_package | deductible_amount | daily_rate | currency | gps_daily_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Sixt+ Subscriptions objects from sixt.com. All fields typed and schema-versioned.
"plan_id": "SP-BMW-3", "vehicle_tier": "BMW 3 Series", "monthly_fee": 799.0, "currency": "USD", "included_mileage": 1000, "setup_fee": 199.0, "commitment_months": 1
| # | plan_id | vehicle_tier | monthly_fee | currency | included_mileage | setup_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Sixt scraper navigates dynamic booking flows, location-specific yield rules, and complex XHR payloads to deliver structured pricing and availability data.
Extract Pay Now and Pay Later rate variants across all vehicle classes, capturing the exact discount delta per location.
Capture airport surcharges, premium location fees, and one-way drop-off penalties applied dynamically at checkout.
Automate searches across 30, 60, or 90-day advance booking windows to map yield curves and seasonal pricing shifts.
Map Sixt's vehicle classes to standard ACRISS codes for direct comparison against competing rental agencies.
Extract daily rates for damage waivers, GPS units, child seats, and toll passes presented during the booking flow.
Scrape global branch metadata including precise coordinates, operating hours, and 24-hour return capabilities.
Monitor monthly recurring rates, mileage limits, and setup fees for Sixt's vehicle subscription service.
Extract specific terms and conditions per quote, including territorial restrictions and excess mileage penalties.
Run one-off fleet audits or configure continuous pipelines at hourly cadences with change-detection diffing.
Brief in. Clean data out.
Provide branch IDs, pickup dates, and rental durations. We design the extraction schema together.
We configure Scrapy crawlers, XHR payload reverse-engineering, and proxy rotation for sixt.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.
Rental pricing is highly dynamic and session-dependent. Here is how we maintain data integrity at scale.
Sixt uses complex GraphQL and REST endpoints for pricing. We reverse-engineer these payloads to bypass the DOM entirely, resulting in faster execution and lower failure rates.
Pricing changes if search sessions are interrupted. We maintain sticky sessions tied to specific residential IPs to ensure quotes remain valid through the entire booking funnel.
We use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid rate limits on high-volume date permutation scraping.
For large fleet tracking, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
OTAs and direct rental brands track Sixt's yield management to adjust their own pricing algorithms.
Analysts track vehicle class availability across regions to identify supply constraints and demand spikes.
Revenue management teams feed ML models with competitor rate data to optimise daily pricing.
Real estate teams map Sixt's physical footprint and airport presence to guide location strategy.
Automotive OEMs analyse Sixt+ pricing and terms to benchmark their own vehicle subscription services.
Insurance providers compare protection package rates and excess reduction fees across the rental sector.
"Sixt's pricing engine adjusts rates dynamically based on fleet utilisation and pickup location — capturing this requires precise, time-series extraction at scale."
Most teams fail at car rental scraping because they underestimate the complexity of date-range permutations and location-specific yield rules. DataFlirt manages the session state, proxy rotation, and payload construction so you receive clean pricing data.
Everything supported by our sixt.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 and retry logic. Playwright manages session state and complex booking flows requiring JavaScript execution.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions for pricing continuity.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About sixt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and fleet information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal data or circumvent authentication walls.
We maintain sticky sessions tied to specific residential IPs. This ensures the pricing context remains consistent throughout the simulated booking flow, capturing accurate rates and ancillary fees.
Yes. We configure pipelines to iterate through matrices of pickup and dropoff dates, allowing you to map yield curves across 30, 60, or 90-day booking windows.
Yes. We extract vehicle tiers, monthly fees, included mileage, and setup costs for the Sixt+ subscription service across supported cities.
Pipelines can be configured for hourly or daily runs depending on your requirements. Real-time streaming is available for targeted location sets.
Our smallest packages start at a defined set of branch locations and date permutations. For global fleet tracking, we price based on volume and delivery frequency.
Yes. We provide a sample run covering specific branches and dates as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off branch audit or a continuous price-monitoring feed across global locations — we scope, build, and operate the pipeline. Tell us what you need.
