Scrape Wizz Air fares, routes and timetables at scale

Wizz Air Scraper
Solutions

Formats, scheduling and delivery

We build the crawler, run it on your schedule and hand over the output. Files arrive as CSV, JSON or XLSX, pushed to S3, Google Cloud Storage, an SFTP drop or a link your own job pulls from. If your team would rather query than parse, the same dataset is exposed as a REST API keyed by origin, destination and date.

Cadence follows how quickly the numbers matter: daily for most fare tracking, tighter for a shortlist of routes inside a booking window, weekly for the station list and route map. The schema is agreed before the first run, and Ryanair, easyJet or any other carrier can be normalised into the same columns so rows compare directly rather than by hand.

Fields we pull from the Wizz Air booking engine

Three booking engine endpoints carry the commercial data.

  • asset/map - the whole network as a single JSON document of roughly 650 KB. On a crawl in late August 2026 it listed 196 stations across 44 countries and 3,290 directional connections. Each station carries iata (the IATA station code), mac (metropolitan area code, for example LON, PAR, ROM, MIL, BUH or WSW), isFakeStation (a city aggregate rather than a real airport), latitude, longitude, currencyCode, countryCode, countryName, shortName, aliases, rank and categories. Each connection carries operationStartDate (the route launch date), isNew, isDomestic and isDirectFlight. Eight entries are metropolitan aggregates flagged isFakeStation - LON, PAR, MIL, ROM, VEN, STO, WSW and OOS - so LON and LTN both appear as bookable origins and route counts double without deduplication.
  • search/timetable - the lowest fare per calendar date for one origin-destination pair, with departureStation and arrivalStation as IATA codes, departureDate, departureDates[] (the departure times actually flown that day), price.amount, price.currencyCode, originalPrice (the pre-discount fare) and hasMacFlight, a flag marking a metropolitan-area itinerary.
  • asset/farechart - a fare curve around an anchor date. Every row holds date, price.amount, currencyCode and classOfService, the fare class or booking class bucket (RBD), with values such as B, L, JK, OP and RS.
  • Availability markers - each price object also carries priceType, which reads price, checkPrice (a live quote is required) or noData (the date is not flown or is sold out), plus exchangedAmount and exchangedCurrencyCode, which stay null unless conversion is requested.
  • Fare bundles and extras - the Basic, WIZZ Go and WIZZ Plus bundle names, and the ancillaries priced beside them: WIZZ Priority (priority boarding with a larger cabin bag), WIZZ Flex (paid date-change flexibility), cabin bag, checked bag and seat selection.

Fields we pull from the Wizz Air booking engine
What only a scheduled crawl can show

What only a scheduled crawl can show

A single pull of Wizz Air fares is one moment in a pricing system that moves daily. The value is in the series, and two structural caps decide how that series gets built.

search/timetable refuses a window wider than 42 days: set from and to further apart and the response is a validation code reading InvalidTimeDateRange, meaning the date range is out of bounds. A full year of one route therefore needs roughly nine chained calls, stitched back together on our side. asset/farechart accepts a dayInterval between 3 and 10 only - anything outside that returns an error stating the interval must be at least 3, or at most 10 - and yields 2n+1 rows around the anchor date. Both limits are fixed, so the crawl plan is designed around them rather than fought.

Repetition also catches what one crawl cannot. Rows arrive with an amount of 0.0 and a priceType of noData or checkPrice when a date is not flown, is sold out or needs a live quote; stored without the priceType, that silently becomes a free seat in your dataset. A pair Wizz Air does not sell is rejected with the code InvalidMarket, meaning the market is not served, so the route list has to be refreshed from asset/map before each cycle - the cheapest way to watch routes appear and disappear. And the API version in the booking engine URL changes on its own schedule, which is the part that quietly breaks in-house scripts.

How wizzair.com is put together

Wizz Air is a European low-cost carrier flying under four operating certificates - Wizz Air Hungary (W6 / WZZ), Wizz Air Malta (W4), Wizz Air UK (W9) and Wizz Air Abu Dhabi (5W). The public site is wizzair.com, with locale paths shaped as /{lang}-{country}. On a recent crawl the page carried 29 hreflang alternates: en-gb acts as the x-default, alongside hu-hu, pl-pl, ro-ro, cs-cz, bg-bg, sq-al, mk-mk, ka-ge, uk-ua and script-qualified variants such as sr-cyrl-rs and bs-latn-ba. The bare domain 301-redirects to the www host.

Very little of that surface holds fares. The front end is a Nuxt server-rendered application whose runtime config points at a separate booking engine at https://be.wizzair.com/{version}/Api, reading 29.13.0 at the time of writing. The version segment moves with each release - older community scrapers pinned 8.7.x, 9.0.1, 10.1.0 or 10.3.0, and each one died at the following deploy. Any crawler with a hardcoded version has a short shelf life.

The indexable side is thin by comparison. On a recent crawl the English sitemap held a few hundred URLs and no per-route pages at all: roughly two hundred /en-gb/cheap-flights-to-{city} destination pages, /en-gb/flights/{country} pages, the Fare Finder, WIZZ Route Map and Timetable tools, and help-centre articles. Legacy /en-gb/flights/{city} URLs redirect to the cheap-flights-to form. Those pages carry a schema.org BreadcrumbList and an AggregateOffer block with lowPrice in EUR - a teaser figure for an entire city, not a bookable fare for a date, and it does not agree with what the booking engine quotes.

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dev_w
25

Developers

customers
500+

Customers worldwide

pages
1 500 000 000+

Pages extracted

stime
15000+

Hours saved for our clients

Plans

Airplane

€199 / one-time

setup fee - included

Data limits100,000
Frequencyone-time
Run timeup to 5 days
Data storing7 days

Helicopter

€169 / mo

setup fee €499

Data limits250,000
Frequencymonthly
Run timeup to 5 days
Data storing14 days

Glasses

€229 / mo

setup fee €499

Data limits1,000,000
Frequencyweekly
Run timeup to 5 days
Data storing30 days

DNA

€549 / mo

setup fee €799

Data limits3,000,000
Frequency3 times daily
Run timesame day
Data storing90 days

What teams do with Wizz Air fare data

Fare monitoring across the European low-cost market is the usual brief. Wizz Air, Ryanair and easyJet overlap on hundreds of city pairs, and competitor fare tracking only works when the same route, on the same departure date, is sampled on the same day for every carrier in the comparison. A single pull answers nothing. A daily series shows who moved first and by how much.

Airports and regional tourism bodies read the route map instead. The operationStartDate and isNew flags on each connection make route launches visible as data, and a connection that vanishes from asset/map between two crawls is a route being dropped. Route development teams watch both directions.

Corporate travel desks and consolidators want the curve rather than a single number: which dates inside a booking window sit under a policy cap, and how Basic, WIZZ Go and WIZZ Plus change the total once a cabin bag or a seat is added. Metasearch and affiliate sites go one step further and rebuild the booking deep link, which is positional rather than query-string based: /{locale}/booking/select-flight/{origin}/{destination}/{outbound date}/{return date or null}/{adults}/{children}/{infants}/{promo or null}.

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Why this work gets handed to ScrapeIt

ScrapeIt is a managed scraping agency, not a tool you install. We write the crawlers, run them on our own infrastructure, watch them when a target shifts, and deliver data your analysts can open. With airline fares that maintenance is most of the work: version segments move, validation rules tighten, station lists change. You receive the dataset and an agreed schema. The breakage stays with us.

FAQ

Can you collect Wizz Air fares for specific routes, dates and passenger counts?

Routes and dates are the core of the job. You give us the origin-destination pairs as IATA station codes and the booking window; we run search/timetable for the lowest fare per calendar date and asset/farechart for the fare curve and fare class around chosen dates. Flight-level detail - departure times, flight numbers, per-passenger pricing and bundle availability - comes from search/search, which sits behind a Kasada bot-management challenge (KPSDK). We scope that part separately and tell you before work starts which fields fall on which side of the line, rather than promising all of them. Collection runs as far ahead as Wizz Air has loaded the schedule for sale; past that horizon the calendar simply returns no data.

How often can Wizz Air prices be refreshed - daily or hourly?

Daily suits most fare tracking and is the usual setting. Tighter refresh is workable on a defined shortlist of routes and dates rather than the whole network, because the 42-day window cap on search/timetable means a single route-year costs roughly nine chained requests - refresh rate and route coverage trade against each other. We size the schedule with you against that arithmetic and run at a request rate that keeps load on the origin low. The route map and station list change slowly and are normally refreshed weekly.

Which currency do the fares arrive in, and can you normalise them?

The booking engine picks currency from the departure station's country, not from the locale in the URL. Budapest to London Luton quotes HUF, Luton to Budapest quotes GBP, Warsaw to Rome Ciampino quotes PLN. The route map lists eighteen currencies across the network: EUR, HUF, PLN, RON, GBP, CHF, CZK, DKK, NOK, SEK, RSD, MKD, BAM, ALL, GEL, MDL, AED and USD. Each price object also has exchangedAmount and exchangedCurrencyCode, which stay null unless a conversion is requested. We keep the native price.amount and price.currencyCode untouched and add a converted column at a stated reference rate and date, so a route quoted in HUF and one quoted in GBP sit in the same comparable table.

What happens when Wizz Air bumps its API version and the scraper breaks?

That is the failure in-house scripts hit most often. The version segment in https://be.wizzair.com/{version}/Api increments with each release, and a client pinned to an old number stops resolving - the reason older community scrapers frozen at 8.7.x, 9.0.1, 10.1.0 or 10.3.0 no longer work. We read the current version from the front end's runtime configuration on every run instead of hardcoding it, and watch for changes behind it: a renamed field, a tightened validation rule, a station that disappears from the map. When something moves, the repair is ours and the feed keeps arriving. Maintenance is part of the service, not a new project.

Do you sign in to a WIZZ account or work around bot checks to reach member prices?

No. We collect publicly available data and do not authenticate. WIZZ Discount Club (WDC) and WIZZ MultiPass prices are shown only to signed-in members, so they are out of scope and what you receive is the public fare. Booking retrieval is out too: Find Booking needs the six-character alphanumeric confirmation code, the PNR, plus the lead passenger surname, or a My WIZZ Account sign-in. The robots.txt file disallows the booking, check-in, itinerary and profile paths under every locale it lists, 29 of them on a recent read, and we honour that. We also do not sell challenge evasion or CAPTCHA solving: the www host sits behind an AWS WAF Human Verification challenge and the flight search call behind Kasada, and where a control blocks automated access we treat it as a project boundary. On legality, gathering public fare information is broadly accepted practice, but the site terms and your intended use are a question for your own counsel, and we scope the work around their answer.

How does it Work?

Step 1 - Make a Request

You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.

Step 2 - Configuring Custom Web Crawlers

Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.

Step 3 - Collect and Deliver

Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.

Step 4 - Maintain and Support

Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.

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