Datasets:

nxtl-esim
/

mobile-data-usage-estimates

license: cc-by-4.0
pretty_name: Mobile Data Usage Estimates
size_categories:
  - n<1K
task_categories:
  - other
tags:
  - mobile-data
  - esim
  - travel
  - usage
  - estimates
  - trip-planning
configs:
  - config_name: activities
    data_files: activity_mb_per_hour.csv
  - config_name: presets
    data_files: daily_presets.csv

Mobile Data Usage Estimates

The activity-level MB/hour assumptions and daily presets used by the open-source trip-data-budget and team-data-planner tools — published so other apps and agents can reuse the same transparent numbers.

Every figure is an estimate, not a measurement.

Tables

`activities` (8 rows)

activity_id	activity	MB / hour
maps	Maps & navigation	8
msg	Messaging & email	10
web	Web browsing	60
music	Music streaming	70
social	Social media (with video)	150
call	Video calls	500
video	Video streaming (SD)	700
hot	Laptop via hotspot	300

`presets` (4 rows)

Hours/day mixes that the tools ship with, plus the daily total after a 1.1× buffer for background updates and sync:

preset	est. MB / day (with buffer)
light	59.4
normal	349.8
heavy	1718.2
remote	2219.8

Streaming assumes standard-definition video; HD can use several times more. Real use varies with app settings and network conditions.

Example uses

In-app data budget calculator: reuse the same MB/hour table so your UX matches the public tool.
AI agent: when a user says "I'll navigate all day and do two hours of calls", multiply the rows instead of inventing rates.
Education / blog: cite a small, versioned table rather than hand-waving "about a gig a day".

Quick load

from datasets import load_dataset
acts = load_dataset("nxtl-esim/mobile-data-usage-estimates", "activities", split="train")
presets = load_dataset("nxtl-esim/mobile-data-usage-estimates", "presets", split="train")
print({r["activity_id"]: r["mb_per_hour"] for r in acts})

import pandas as pd
acts = pd.read_csv("activity_mb_per_hour.csv").set_index("activity_id")["mb_per_hour"]
hours = {"maps": 1, "msg": 3, "web": 1, "music": 1, "social": 1}  # "normal" mix
mb = sum(acts[k] * h for k, h in hours.items()) * 1.1
print(round(mb, 1), "MB/day estimate")

Methodology

Copied from the constants embedded in trip-data-budget (ACTS, PRE, 10% buffer). No separate lab measurement was performed for this release. Where a figure is used in product copy it is labelled as an estimate.

Limitations

Not a substitute for measuring your own apps on your own network.
No per-app breakdown (Instagram vs TikTok, Zoom vs Meet, etc.).
Video is SD-only in the table; there is no HD/4K row.
Presets are starting points, not personas backed by survey data.

Licence and credit

CC BY 4.0. Attribute "NXTL mobile data usage estimates".

Live tool: trip-data-budget · sibling planner: team-data-planner · published by NXTL eSIM

Citation

@misc{nxtl_mobile_data_usage_estimates_2026,
  title        = {Mobile Data Usage Estimates},
  author       = {NXTL},
  year         = {2026},
  howpublished = {Hugging Face dataset},
  note         = {Assumptions from trip-data-budget / team-data-planner}
}