I have a travel SEO site with 75 destination datasets. Here's what the daily-cost number actually means.

I run http://vientapps.com, a small travel research site I've been building solo for SEO + tool distribution. Part of the play is structured destination guides. JSON files with daily-cost tiers, season data, packing lists, and itinerary blocks for 75 cities so far. The dataset feeds the destination pages, the packing-list generator, and a group-travel tool called Roamly.

This week I finally sat down and pulled one number across all 75 files: budget-tier daily cost in USD. The cheapest is Ho Chi Minh City at $20/day. The most expensive is Maui at $170/day. Ratio: 8.5x.

What surprised me wasn't the spread, it was the pattern:

None in Western Europe, none in Oceania, none in North America outside Mexico.

19 US destinations: 18 sit above the global median. The single exception is Portland at $70/day.

Europe splits sharply east vs north. Krakow $40, Reykjavik $120. Same continent, 3x ratio.

Hawaii is the outlier even by US standards. Maui $170 and Honolulu $130 both beat SF and NYC on daily cost.

The thing I'd flag for other builders: don't trust the $/day metric alone. I wrote a whole counterpoint section because once you add fixed costs (flights, visas, gear), the cheap-daily destinations stop being cheaper at 7-day trip lengths. They only win at 14+ days. This is why digital nomads cluster in Chiang Mai and Medellin, the fixed-cost ratio amortizes over months.

Full post + downloadable CSV:
https://vientapps.com/blog/75-destinations-daily-cost-ranking-2026/

Happy to share the methodology and the dataset structure if anyone is building something similar. The biggest unsolved problem on my side is sourcing clean flight-pricing data so I can ship a trip-total ranking next. Suggestions welcome.