We extract peer-to-peer car listings, availability calendars, hourly pricing, and owner metrics from Getaround. 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 Vehicle Listings objects from getaround.com. All fields typed and schema-versioned.
"vehicle_id": "v_839201", "make": "Toyota", "model": "Prius", "year": 2018, "category": "Compact", "instant_book": true, "getaround_connect": true, "rating": 4.8, "trips_count": 142
| # | vehicle_id | make | model | year | category | transmission |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from getaround.com. All fields typed and schema-versioned.
"vehicle_id": "v_839201", "hourly_rate": 8.5, "daily_rate": 65.0, "mileage_limit_per_day": 200, "extra_mileage_fee": 0.45, "cleaning_fee": 25.0, "currency": "USD", "pricing_timestamp": "2026-08-14T10:00:00Z"
| # | vehicle_id | hourly_rate | daily_rate | weekend_rate | deposit_fee | mileage_limit_per_day |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability objects from getaround.com. All fields typed and schema-versioned.
"vehicle_id": "v_839201", "date": "2026-08-20", "start_time": "09:00:00", "end_time": "17:00:00", "is_available": true, "price_for_block": 68.0, "minimum_duration_hours": 2, "timezone": "America/Los_Angeles"
| # | vehicle_id | date | start_time | end_time | is_available | price_for_block |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Owner Profiles objects from getaround.com. All fields typed and schema-versioned.
"owner_id": "u_94821", "name": "Alex M.", "join_date": "2021-04-12", "response_rate": 98.5, "response_time_mins": 15, "total_trips": 412, "vehicles_listed": 3, "verified_identity": true
| # | owner_id | name | join_date | response_rate | response_time_mins | total_trips |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from getaround.com. All fields typed and schema-versioned.
"review_id": "r_1049284", "vehicle_id": "v_839201", "reviewer_name": "Sarah T.", "rating": 5, "trip_date": "2026-07-15", "review_text": "Car was clean and pickup was easy using the app.", "created_at": "2026-07-18T14:22:10Z"
| # | review_id | vehicle_id | owner_id | reviewer_name | rating | trip_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Getaround scraper handles map-based pagination, dynamic pricing grids, and availability matrix extraction with full circumvention of location-based rate limits.
Extract make, model, year, transmission, seat count, and Getaround Connect hardware status for every listed vehicle.
Capture base hourly rates, daily caps, weekend multipliers, and mandatory fees timestamped per crawl.
Extract block-level calendar availability to determine true supply versus booked inventory across any city.
Capture precise latitude and longitude for vehicle home locations to map supply density.
Aggregate host profiles to identify power-users versus single-vehicle casual owners based on listed inventory.
Extract paginated trip reviews, star ratings, and host response rates to measure fleet quality.
Identify which vehicles support Instant Book versus manual approval to gauge platform friction.
Extract daily included mileage allowances and excess distance penalty fees per vehicle class.
Run continuous pipelines at daily cadences with change-detection diffing for pricing and calendar updates.
Brief in. Clean data out.
Provide target cities, zip codes, or coordinate bounding boxes. We design the extraction schema together.
We configure Scrapy crawlers, residential proxy rotation, and map-grid pagination logic for getaround.com.
Schema validation, coordinate precision checks, and calendar anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting mobility data requires reverse-engineering map APIs and handling multi-dimensional calendar states. Here is how we build it.
Getaround limits search results per map view. We divide target cities into 1km coordinate grids, iterating through micro-bounding boxes to ensure 100% vehicle discovery without truncation.
Vehicle availability is not a boolean. We parse the calendar matrix to extract block-level open hours, minimum trip durations, and dynamic pricing rules applied to specific days.
Aggressive querying of the search API triggers IP bans. We distribute calendar and pricing requests across US-based residential proxy pools, normalising request headers to match mobile app traffic.
For fleet tracking, we maintain a hash index of last-seen calendar states. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
We target the underlying GraphQL and REST endpoints powering the Getaround frontend, providing higher data density and resilience against visual UI changes.
Urban planners and mobility analysts map peer-to-peer car supply density against public transit gaps.
Power-hosts and fleet managers monitor competitor pricing and availability to optimise their own hourly rates.
Actuaries analyse vehicle types, trip frequencies, and geographic concentration to refine peer-to-peer insurance products.
Traditional rental companies (Avis, Hertz) track Getaround inventory and pricing to adjust local branch strategies.
Data science teams model how weekend demand, local events, and weather impact peer-to-peer car rental rates.
Private equity firms track active vehicle counts and trip velocity to evaluate marketplace liquidity.
"Getaround holds the granular pulse of peer-to-peer mobility supply, but extracting calendar-level availability requires bypassing strict map-bound rate limits."
Most teams underestimate the investment required: reliable Getaround scraping requires residential proxies, map bounding box pagination, availability matrix parsing, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our getaround.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.
We target Getaround internal API endpoints directly, handling token generation and payload signing to extract structured JSON rather than parsing HTML.
PostGIS handles bounding box generation and grid division, ensuring crawlers systematically cover target geographies without overlapping requests.
Pipelines run on AWS Lambda for parallel map-grid traversal. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About getaround.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Getaround is generally permissible. DataFlirt targets only public, non-authenticated vehicle listings, pricing, and host profiles. We do not extract private renter data, circumvent authentication walls, or violate GDPR. Clients should review Getaround Terms of Service and consult legal counsel.
Getaround limits the number of vehicles returned in a single map view. We divide target regions into 1km coordinate grids, iterating through these micro-bounding boxes to ensure complete extraction without truncation.
Yes. We parse the calendar matrix to extract block-level availability, minimum duration requirements, and date-specific dynamic pricing up to 30 days in advance.
Pipelines can be configured to run daily or at custom intervals. We capture the exact hourly rate, daily caps, and fees at the time of the crawl.
We extract the latitude and longitude coordinates provided by Getaround for the vehicle home location, which is typically precise enough for neighborhood-level density mapping.
Our smallest packages start at tracking specific cities or zip codes with weekly delivery. For national coverage or custom schema requirements, we price based on geographic volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off geographic supply dump or a continuous price-monitoring feed across major cities, we scope, build, and operate the pipeline. Tell us what you need.
