---
title: "Pixeltable vs Daft: application schema vs multimodal dataframe"
description: "Daft (Eventual) is a multimodal dataframe. Pixeltable is a live application schema with indexes and HTTP. Often and, not or. Last reviewed Oct 2026."
keywords:
  - Pixeltable vs Daft
  - Daft alternative
  - Eventual Daft
  - multimodal dataframe
  - multimodal data platform
url: "https://www.pixeltable.com/compare/pixeltable-vs-daft"
---

# Pixeltable and Daft

Daft, from Eventual, is an open-source multimodal dataframe: native image, video, audio, and embedding types, laptop to cluster. Pixeltable is a live application schema: insert a row and computed columns, indexes, and HTTP follow. Batch vs the app. Often and, not or.

## Summary

### Pixeltable

- Typed Image, Video, Audio, Document columns in a catalog
- Computed columns run on insert; unchanged rows stay cached
- EmbeddingIndex and FastAPIRouter in the same file
- Not a Spark-shaped dataframe over petabytes

### Daft

- Multimodal dataframe: tables, images, text, embeddings in one Python API
- download().decode_image().resize() on URL columns; UDFs and GPU models
- Local or distributed; Rust engine, no JVM
- Write results to a lake, warehouse, or vector DB you add; not an app catalog

## Comparison

| Feature | Pixeltable | Daft |
| --- | --- | --- |
| What it is | Application schema: store, transform, index, serve | Multimodal dataframe for batch AI workloads |
| Execution model | Insert or edit a row; dependents of that row recompute | Load, transform, write a dataframe; scale the job |
| Cluster-scale media scans | Not a distributed dataframe over a lake | The product: laptop to cluster without changing the API |
| Durable catalog | Tables, history, and indexes you query later | A job that reads and writes; persistence is the sink you choose |
| Vector index | EmbeddingIndex declared on the column | Compute embeddings, then write to a vector DB or lake |
| HTTP serving | FastAPIRouter declared next to the tables | Not a web framework; put FastAPI or a notebook in front |

## Images: insert vs scan

Pixeltable: an Image column and a CLIP index; insert embeds that row. Daft: from_pydict, download, decode_image, resize — then collect. Last reviewed: Oct 2026. Daft docs: https://docs.daft.ai/en/stable/modalities/images/

### Pixeltable

```python
import pixeltable as pxt
from pixeltable.functions.huggingface import clip

TableModel = pxt.model_base()
clip_embed = clip.using(model_id='openai/clip-vit-base-patch32')

class Frames(TableModel, name='frames'):
    image: pxt.Image
    caption: pxt.String | None
    __indexes__ = [pxt.EmbeddingIndex(image, embedding=clip_embed)]

if __name__ == '__main__':
    # After: pxt schema update app.py vision
    frames = pxt.get_table('vision.frames')
    frames.insert([{'image': 'https://example.com/frame.jpg', 'caption': None}])
    sim = frames.image.similarity(string='whiteboard diagram')
    frames.order_by(sim, asc=False).limit(5).select(frames.image, frames.caption)
```

### Daft

```python
import daft
from daft import col

df = daft.from_pydict({
    'urls': ['https://example.com/frame.jpg'],
})
df = df.with_column(
    'image',
    col('urls').download(on_error='null').decode_image().resize(224, 224),
)
df.collect()
# Write to a lake or vector store you add. HTTP is still yours.
```

## When to choose Pixeltable

- **The pipeline is the schema**: Insert a row; transforms, indexes, and HTTP follow. A coding agent writes one file. Cloud is Limited Beta.
- **You still want Daft for backfills**: Scan a lake with Daft, then insert into Pixeltable when the live app should own the rows.

## When to choose Daft

- **The job is a cluster scan**: Petabytes of images or video, GPU UDFs, write to Iceberg or a vector DB. Pixeltable is extra.
- **You already think in dataframes**: Pandas- or Spark-shaped API, no application catalog required. Docs: https://docs.daft.ai/en/stable/

## Making the right choice

### And, not or

- Daft is not a Pixeltable competitor for serving HTTP. It is a batch engine. Ray Data is the same aisle.
- Do not pick Pixeltable as a Daft replacement for distributed scans.
- Last reviewed: Oct 2026. Daft: docs.daft.ai. Roundup: /learn/best-multimodal-data-platforms

## FAQ

### Is Pixeltable a Daft alternative?

Only if you needed a live application schema and were about to use Daft as that catalog. Daft is a multimodal dataframe. Pixeltable is tables, computed columns, indexes, and HTTP. Many teams run both.

### How does a dataframe differ from an application schema?

A dataframe loads, transforms, and writes a batch. An application schema stores rows, runs declared columns when those rows change, and can serve HTTP.

### Where were these facts checked?

Last reviewed: Oct 2026. Daft: https://docs.daft.ai/en/stable/ and https://docs.daft.ai/en/stable/modalities/images/ (from_pydict, download, decode_image, resize). Pixeltable: docs.pixeltable.com.