May 21, 2026 · 8 min read · Comparisons
TravelMindsAI vs scraping Wikipedia and Wikivoyage.
The first instinct of every team building a travel product is to scrape Wikipedia. The infoboxes look structured. Wikivoyage has real itineraries. It's free. What could go wrong? Several things — and the cost of finding out is usually three sprints in.
The appeal
Wikipedia has a city article for almost every city on Earth, with a sidebar infobox containing population, coordinates, elevation, and administrative parent. Wikivoyage has trip itineraries, "see" lists, and seasonal notes. Both are free, both are well-maintained, and the data is structured enough that a motivated developer can extract it with BeautifulSoup over a weekend.
For a hobby project this works fine. For a product you ship to customers, the picture changes.
Cost 1: TOS and rate limits
Wikipedia's content is CC-BY-SA, which is permissive — you can use it commercially with attribution and share-alike. Scraping the rendered HTML at scale, however, runs into the Wikimedia Foundation's rate limits and bot policy. The legal answer is "use the database dumps." The dumps are fine, but they are multi-gigabyte XML files updated on a delay, and parsing them is its own engineering project.
Wikivoyage is similar. CC-BY-SA on content, dumps available, rendered-HTML scraping discouraged.
Cost 2: Brittle parsers
Wikipedia infoboxes are not a schema. They're free-form templates that editors fill in by hand. The infobox for Mumbai might use "Settlement" template; for Tawang it might use "Indian jurisdiction" template; for a small village it might use no template at all. Field names drift across articles. "elevation_m" vs "elevation" vs "altitude." "subdivision_type1" vs "state."
A parser that handles five Indian cities elegantly will fail on the sixth. A parser that handles a thousand will still miss edge cases. You will spend more time on parser robustness than on your actual product.
Cost 3: No Indian admin hierarchy
A Wikipedia infobox tells you the city is in "Maharashtra." It does not give you a stable identifier. It does not give you the district. It does not give you the tehsil. It does not give you the ISO subdivision code (IN-MH). You can derive these by walking up the article tree, but you're now writing a graph crawler on top of an HTML parser, and the crawler also breaks on edge cases.
For Indian travel products specifically, admin hierarchy is where the value is. Two cities named the same in different districts is the rule, not the exception. Mathura the village in Punjab is not Mathura the pilgrimage city in Uttar Pradesh. A scraper that returns "Mathura, India" without the state and district is worse than no data — it produces confidently wrong output downstream.
Cost 4: No heritage status, no circuits
Wikipedia mentions UNESCO inscription years in prose; sometimes. It does not give you a clean list of ASI Centrally Protected Monuments — that list lives in ASI's own publications. It does not tell you which cities are on the Buddhist Circuit in what order. You can extract some of this from articles by hand. You cannot extract it reliably at scale.
Wikidata is the partial exception
Wikidata is the structured-data sibling of Wikipedia. It has stable identifiers (Q-numbers), proper schemas, and a SPARQL endpoint. For some join-key needs it's a usable alternative. Wikidata is one of the public sources we ingest, and we will not pretend otherwise.
The catch: Wikidata's coverage of Indian destinations is uneven. Major cities are well-modeled. Smaller towns and monuments are partial. ASI status, when it exists, is often in a free-text description rather than a structured property. You can use Wikidata as a starting point. You will still spend weeks on the join with ASI, UNESCO, and tourism circuit data.
The honest tradeoff
Scraping Wikipedia and Wikivoyage is the right answer when (a) your product is a hobby project, (b) you are okay with the data being approximate, or (c) you have a research use case where accuracy doesn't drive revenue. For a shipped product where "city is in the wrong state" is a customer-visible bug, build on a structured API.
$49 a month is cheaper than the engineer-weeks the scraper will cost you, and it ships with the joins already done.