---
title: "Pixeltable vs LanceDB: pipeline vs the index"
description: "LanceDB wins ANN and Lance layout. Pixeltable wins incremental computed pipelines and HTTP. Export with pxt.io.export_lancedb when you want both."
keywords:
  - Pixeltable vs LanceDB
  - LanceDB alternative
  - multimodal vector database
  - Lance format
  - Python AI backend
url: "https://pixeltable.com/compare/pixeltable-vs-lancedb"
---

# Pixeltable vs LanceDB

LanceDB is a strong multimodal lake and ANN index. Pixeltable is a live application schema: computed columns, provider UDFs, and HTTP. Often and, not or. Export embeddings with pxt.io.export_lancedb when the index is the product.

## Summary

### Pixeltable

- Computed columns run ffmpeg, Whisper, CLIP, and chat on insert
- EmbeddingIndex stays on the source row; no upsert glue
- FastAPIRouter in the same file
- Export a query to LanceDB when you want that serving engine

### LanceDB

- Lance format: columnar, versioned, multimodal blobs + vectors
- ANN tuned for large embedding tables
- Embedded or cloud; SQL-style filters on stored metadata
- You still write chunking, model calls, and HTTP around it

## Comparison

| Feature | Pixeltable | LanceDB |
| --- | --- | --- |
| What it is | Application schema: store, transform, index, serve | Multimodal lake and vector engine on Lance |
| Vector query | EmbeddingIndex + similarity(string=query) | ANN the product is built around; usually faster at large scale |
| Media pipelines | Iterators and computed columns; insert runs the work | Store multimodal data; processing is UDFs or jobs you write |
| Embedding sync | Index is a schema object; insert and delete keep it current | Embedding functions and backfill; you own the re-embed job |
| HTTP serving | FastAPIRouter declared next to the tables | Not a web framework; you put FastAPI or a notebook in front |
| Lake / format | Catalog plus media refs; not a Lance lakehouse | Lance fragments, versioning, and ecosystem around the format |
| Embedded deploy | Local dir or Pixeltable Cloud | Process-local LanceDB with a small footprint |

## Document index

Pixeltable: chunking is a view, the index is on the class. LanceDB: embedding registry and search. Apply Pixeltable with pxt schema update app.py search.

### Pixeltable

```python
import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer

TableModel = pxt.model_base()
embed = sentence_transformer.using(
    model_id='sentence-transformers/all-MiniLM-L6-v2'
)

class Docs(TableModel, name='docs'):
    document: pxt.Document
    title: pxt.String

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(Docs.document, separators='sentence', limit=512),
):
    __indexes__ = [pxt.EmbeddingIndex(text, embedding=embed)]

# pxt schema update app.py search
docs = pxt.get_table('search.docs')
docs.insert([{'document': 'handbook.pdf', 'title': 'Handbook'}])
chunks = pxt.get_table('search.chunks')
sim = chunks.text.similarity(string='refund policy')
chunks.order_by(sim, asc=False).limit(5).select(chunks.text, chunks.title)
```

### LanceDB

```python
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry

db = lancedb.connect('./lancedb')
func = get_registry().get('sentence-transformers').create(
    name='all-MiniLM-L6-v2'
)

class Document(LanceModel):
    title: str
    content: str = func.SourceField()
    vector: Vector(func.ndims()) = func.VectorField()

table = db.create_table('documents', schema=Document)
table.add([{'title': 'Handbook', 'content': '...'}])
hits = table.search('refund policy').limit(5).to_pandas()
# Chunking, ffmpeg, and HTTP are still outside this table.
```

## Hand the index to LanceDB

When LanceDB should serve the vectors, export a Pixeltable query. Requires pip install lancedb pylance.

### Pixeltable

```python
from pathlib import Path
import pixeltable as pxt

chunks = pxt.get_table('search.chunks')
pxt.io.export_lancedb(
    chunks.select(chunks.text, chunks.title),
    Path('lancedb'),
    'chunks',
    if_exists='overwrite',
)
```

### LanceDB

```python
import lancedb

db = lancedb.connect('./lancedb')
table = db.open_table('chunks')
hits = table.search('refund policy').limit(5).to_pandas()
# Pixeltable remains the system of record for source files and compute.
```

## When to choose Pixeltable

- **The pipeline is the schema**: Documents, frames, transcripts, provider models, and an HTTP route in one file. Incremental column add without a re-embed script you maintain.
- **You still want LanceDB for serving**: Process in Pixeltable. export_lancedb when the contract is a Lance table. Integration notes: https://pixeltable.com/blog/pixeltable-lancedb-integration

## When to choose LanceDB

- **The index is the product**: You already have embeddings. You need ANN, Lance layout, or an embedded process-local store. Pixeltable is extra.
- **Lake-scale vector analytics**: Lance fragments, versioning, and query engines built around that format. Do not pick Pixeltable as a Lance replacement.

## FAQ

### Is Pixeltable a LanceDB alternative?

Sometimes. If you need a live multimodal pipeline and HTTP, Pixeltable can be the whole backend. If you need a Lance lake and ANN serving, LanceDB stays. Many teams run both and export.

### Does LanceDB only do single-modal search?

No. That was a stale claim on this page. LanceDB stores multimodal data and has embedding functions (including vision models). Pixeltable’s edge is incremental computed columns and serving, not “Lance cannot do images.”

### Does Pixeltable include hundreds of AI functions?

No. Provider UDFs cover OpenAI, Anthropic, Hugging Face, and similar. There is no 200+ catalog. Do not treat a count as the differentiator.