Netflix has no single catalogue. The same title id is live in Japan and absent in Germany, so the only honest dataset is a title-by-country-by-week matrix, not a list.
You set the scope: which countries, which genre hubs, which title ids, and whether you want one snapshot or a repeating run that becomes a change log of arrivals and removals. We build the crawler, run it on your schedule and deliver CSV, JSON, XLSX or an API endpoint. Field names, the country list and the Top 10 matching rules are agreed before the first run.
The engineering is ours. Because the storefront follows the exit point, a country matrix needs exits in every market on your list; anti-bot handling, proxy rotation and CAPTCHA solving are part of the managed service. We work only on pages a signed-out visitor can reach and never sign in to an account. We do not touch the video stream and we do not go near content protection, and we collect no account, profile or viewing records. Netflix terms of use prohibit automated collection - we say so plainly, and anything contested should go past your own counsel first.
Title pages are open to a signed-out visitor: no account, no player, no paywall in front of the metadata. This is what we extract for each Netflix title id.
Two honest gaps, stated up front. Feature films carry no runtime on the public page: year and rating are shown, and only the trailers state a length, while episode runtimes are present for series. And the New and Popular list sits behind a sign-in, so arrivals and departures are reconstructed by comparing catalogue snapshots rather than read off a page.
Netflix publishes genuine viewing data, and it is the strongest open series any streamer puts out. The weekly Top 10 arrives as downloadable tables in TSV and XLSX, not only as a web page with a week picker.
Twice a year Netflix also publishes What We Watched, a spreadsheet listing hours viewed for every title watched more than about 50,000 hours, its premiere date and whether it was available globally; runtime and views were added to it later, and films and series were separated out.
There is one catch in these files and it decides the engineering. They key on the show title as free text - there is no title id column anywhere in them. Joining a ranking row to a catalogue record means matching names across localised titles, season labels and punctuation. We run that join and return a table that carries the numeric id, which is what makes a rank usable next to availability and catalogue churn.
Netflix runs one domain and hundreds of storefronts. A request lands on the storefront of the country it is made from, and the country segment in the address does not override that: ask for the US or Japanese path from a European exit point and the European storefront is what comes back. Catalogue, artwork, audio tracks, subtitle lists, maturity ratings and plan prices are all settled at that moment.
The title id survives all of it. Every film and series is a plain integer in the /title/ address, and that integer is the same in every market, which makes it the join key for the whole dataset. Netflix publishes the proof itself: the sitemap index holds about 26,200 per-title sitemaps as of September 2026, one per id, and each lists that same id under every locale where the title is licensed, with hreflang alternates beside it.
Coverage is where the storefronts split apart. A global original appears under 386 locale addresses spanning 245 country and territory codes. A Japanese licence appears under two, jp and jp-en. A Ukrainian licence under ua and ua-ru. A Southeast Asian package covers eleven countries and stops there. Request that Japanese id from a German exit point and Netflix answers with an error page rather than a title record - the id is valid, the page simply does not exist in that country. That single behaviour is why a Netflix scraper has to be built as a grid of exit points rather than as one crawler.
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The people who pay for Netflix data treat a catalogue as an asset register, not as a browse list.
None of them needs a title list. They need the same title observed in many countries on many dates, because the answer to whether something is on Netflix only means anything with a country and a date attached to it. That is the dataset we extract Netflix titles into: one row per title, per country, per snapshot, with first-seen and last-seen stamps derived from the run history.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed web scraping agency. You describe the dataset, we build and operate the Netflix scraper and hand back files or an API; there is no tool for you to run and no proxy bill for you to manage. A Netflix scraping project usually opens with a paid sample - a few hundred title ids across three or four countries - so you can test the fields and the country matrix against your own records before committing to a schedule. Delivery goes to S3, GCS, an SFTP drop, a webhook or a database you nominate.
No. The old public Netflix API was retired long ago and there is no developer portal left to register with; the partner help centre that remains serves production partners on delivery, quality control, artwork and dubbing, not data buyers. The open exceptions are the Top 10 tables and the twice-yearly engagement spreadsheet, both of which anyone can download. Everything else in the catalogue comes from public title pages and genre hubs. What we deliver is a Netflix API of our own: an endpoint over the extracted dataset, with your field names and your refresh schedule.
Because licences are sold by territory and by window, not globally. Netflix exposes this openly: the per-title sitemap for a global original lists hundreds of locale addresses, while a regional licence lists two or three. A Japan-only title returns an error page from a European exit point even though the id is correct. Dates differ as well - the same series can arrive months apart in two markets and leave one while staying in the other. That is why the deliverable is a matrix of title, country and date rather than a single catalogue file.
Any market with a Netflix storefront, because coverage is a question of exit points rather than of parsing. You give us the country list; we run the same title set from each one and record what resolves and what does not. Departures are derived, not read: there is no public leaving-soon page, so a title present in one snapshot and absent from the next is flagged as removed, with the date pair that brackets it. The same comparison produces arrivals, so catalogue churn falls out of the run history for free.
Daily, weekly or monthly, whichever matches how you use it. Weekly suits availability tracking and lines up with the Top 10 release cycle; daily makes sense when you are watching a specific slate or a launch window. Output is CSV, JSON, XLSX or a REST endpoint, delivered to S3, GCS, SFTP, a webhook or straight into a database. Every row is stamped with the country code and the snapshot date, so successive runs stack into a history rather than overwriting each other.
Be clear-eyed about it. The Netflix terms of use prohibit robots, spiders, scrapers and any data gathering or extraction method, and the robots file disallows crawling by default outside a named list of search engines. We work only on pages a signed-out visitor can reach, we never log in, we do not touch the video stream or content protection, and we collect no account or viewing data belonging to any individual. We are a contractor, not your legal adviser: take the terms and your intended use to your own counsel before we start.
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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