---
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](https://nxtlsim.github.io/trip-data-budget/) and [team-data-planner](https://nxtlsim.github.io/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

```python
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})
```

```python
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](https://nxtlsim.github.io/trip-data-budget/) · sibling planner: [team-data-planner](https://nxtlsim.github.io/team-data-planner/) · published by [NXTL eSIM](https://nxtlsim.com/)

## 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}
}
```