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Rush to Indian Street Food 🌢️πŸ”₯

Rush to Indian Street Food 🌢️πŸ”₯

A Roblox experience owned by Lowbuck Studio.

SimulatorENfoodqueuecollection

Description (written by the developer; not a statement by this register)

🀯 OMG! The most viral Indian street food is here, and EVERYONE wants a taste! Can you make it to the front of the line to try the most viral foods? 🌢️πŸ”₯ πŸš— Race to the front before everyone else! 🧍 Wait in the LONG queue with hungry customers! 🍜 Finally try the most viral Indian street foods! 🌢️ Discover new foods, from Pani Puri to spicy Momos! 😈 Watch out for chaos, pranks, and players trying to steal your spot! 🎁 Collect and unlock different Indian street foods! ⭐ Try every dish, from COMMON to LEGENDARY! πŸ† Become the ultimate Indian street food explorer!

Last updated by the developer 25 Oct 2026, 13:51 UTC.

16
playing (measured 30 Oct 2026, 16:00 UTC)
47,990
lifetime visits
26%
liked (54 πŸ‘ / 155 πŸ‘Ž)
529
favourites (11.0 per 1k visits)
19 October 2026
created (per Roblox)

Play on Roblox β†’

Live players over time

Growth

Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart β€” so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log β€” every players count we have recorded
Measured (UTC)PlayersChange
30 Oct 2026, 16:0013first reading

Concurrent players at each of our measurements (insert-on-change).

Monetization β€” game passes

2x Rupees
79 R$
Troll Shield
49 R$

Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.

Badge funnel

Hospitalized Β· other2,375 Β· 27.3% win
so close... Β· other1,251 Β· 13.3% win
You survived Β· progression54 Β· 0.90% win

How many players have ever earned each badge β€” a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.